diff --git a/Papers/NAR_Update_2026/analysis/README.md b/Papers/NAR_Update_2026/analysis/README.md
index 7fe5b052..a00d9623 100644
--- a/Papers/NAR_Update_2026/analysis/README.md
+++ b/Papers/NAR_Update_2026/analysis/README.md
@@ -39,6 +39,9 @@ Both stayed in `Scripts/Thermodynamics/ProtonationEvidence/`.
 | `make_figure2_thermodynamics.py` | Figure 2, `../figures/figure2_thermodynamics.pdf` |
 | `make_figure3_direction.py` | Figure 3, `../figures/figure3_direction.pdf` |
 | `grace_style.py` | shared Grace/xmgrace visual style used by both |
+| `pathway_distribution_of_growth.py` | where the 2020→2026 growth landed at MetaCyc class level — reviewer 1 comment 1 |
+| `review_transport_and_llm.py` | what the transport caveat costs, and what the LLM ensemble is worth — reviewer 1 comment 2, reviewer 2 comment 2 |
+| `population_basis_table.py` | every count the manuscript quotes, on both populations, side by side — the audit trail for the live-basis conversion |
 
 The graphical abstract is submitted as a **separate file** and is deliberately
 never `\includegraphics`'d into `main.tex` — NAR requires it uploaded on its
@@ -48,7 +51,11 @@ Findings written up from these live in `../data/`, dated -- untracked, local to
 
 ## Running them
 
-They read caches and fitted parameters from the eQuilibrator working tree, which
+The two reviewer-response scripts are the exception to the paragraph below:
+they read only `Biochemistry/` and the git history of this repository, so they
+run anywhere with no `EQUILIBRATOR_DIR`.
+
+The rest read caches and fitted parameters from the eQuilibrator working tree, which
 is far too large to commit — `data/` there is several gigabytes. That location is
 `$EQUILIBRATOR_DIR`, defaulting to the analysis host's path. Paths into this
 repository derive from the file location and need no configuration.
diff --git a/Papers/NAR_Update_2026/analysis/figure_common.py b/Papers/NAR_Update_2026/analysis/figure_common.py
index 500f0c9a..53098f15 100644
--- a/Papers/NAR_Update_2026/analysis/figure_common.py
+++ b/Papers/NAR_Update_2026/analysis/figure_common.py
@@ -28,6 +28,29 @@
 
 FIGDIR = Path(__file__).resolve().parent.parent / "figures"
 
+# ---- population -----------------------------------------------------------
+# Obsolete records are excluded from every figure. The 2026 draft mixed two
+# populations -- "34% growth in compounds" counted all records, "55% in
+# reactions" counted 2026 totals against a 2020 baseline with obsolete rows
+# stripped -- and the figures silently counted all records throughout. One
+# flag, threaded through every loader below, so a figure cannot disagree with
+# the text. Flip it to False to reproduce the all-records numbers.
+LIVE_ONLY = True
+
+
+def _live(records):
+    """Filter a list of loaded records to the reporting population."""
+    if not LIVE_ONLY:
+        return records
+    return [r for r in records if r.get("is_obsolete") not in (1, "1")]
+
+
+def _live_rows(rows, col="is_obsolete"):
+    """Same, for DictReader rows off a TSV."""
+    if not LIVE_ONLY:
+        return rows
+    return [r for r in rows if r.get(col) != "1"]
+
 
 def save(fig, name):
     """Write one figure to ../figures/<name>.pdf and report the path."""
@@ -55,6 +78,13 @@ def _pka_provenance():
     and ModelSEED_Reaction_Energies.tsv. Both ship, so a reader can reproduce
     every percentage in the protonation paragraph.
 
+    COLOUR, 2026-09-22: "no ionizable site" was NEUTRAL grey, the same family
+    as "no structure", which merged a chemistry RESULT (Marvin ran and found no
+    dissociable proton between pH -2 and 16) with a curation GAP (there is no
+    structure to run on). Reviewer 1 asked for a different scheme for this
+    panel; the result now takes its own categorical slot and only the genuine
+    absence stays grey.
+
     REWRITTEN 2026-09-08 for the Marvin 26.1 rebuild. The split is no longer
     open-vs-proprietary -- every resolved ladder is ChemAxon-derived now -- but
     NEW-RUN vs CARRIED-OVER, which is the distinction that survived the change.
@@ -138,7 +168,7 @@ def norm(db, e):
 
     allc, struct = set(), set()
     for f in sorted(_glob.glob(str(root / "Biochemistry/compound_*.json"))):
-        for c in _json.load(open(f)):
+        for c in _live(_json.load(open(f))):
             allc.add(c["id"])
             if c.get("smiles") or c.get("inchikey"): struct.add(c["id"])
 
@@ -161,12 +191,12 @@ def norm(db, e):
     # earning a fourth colour. The release split is a sentence in the text.
     by_cpd = [("Marvin", cc["26.1 InChI"] + cc["23.4 InChI"], BLUE, ""),
               ("from SMILES", cc["26.1 SMILES"] + cc["23.4 SMILES"], BLUE, "xxx"),
-              ("no ionizable site", cc["no site"], NEUTRAL, ""),
+              ("no ionizable site", cc["no site"], ORANGE, ""),
               ("no structure", cc["no structure"], GRID, "")]
 
     rx = _c.Counter()
     for f in sorted(_glob.glob(str(root / "Biochemistry/reaction_*.json"))):
-        for r in _json.load(open(f)):
+        for r in _live(_json.load(open(f))):
             st = r.get("stoichiometry")
             if not isinstance(st, list) or not st: continue
             v = {cls.get(x["compound"], "no structure") for x in st}
@@ -176,7 +206,7 @@ def norm(db, e):
             else: rx["none"] += 1
     by_rxn = [("Marvin", rx["inchi"], BLUE, ""),
               ("from SMILES", rx["smi"], BLUE, "xxx"),
-              ("no ionizable site", rx["none"], NEUTRAL, ""),
+              ("no ionizable site", rx["none"], ORANGE, ""),
               ("no structure", rx["inc"], GRID, "")]
     return by_cpd, by_rxn
 
@@ -185,17 +215,19 @@ def _growth():
     """Figure 1A: compounds carrying an alias from each structure source,
     2020 against 2026. DERIVED from the shipped alias file on both sides.
 
-    The 2020 column used to be transcribed from an untracked MANUSCRIPT.md and
-    was wrong for both sources that existed then -- MetaCyc 19,138 against a
-    true 19,172, KEGG 17,760 against 17,793. It is now read from
-    Unique_ModelSEED_Compound_Aliases.txt at the last commit of 2020
-    (fd6c7849, 2020-11-10), six weeks after the paper appeared online, so the
-    figure cannot drift from the repository again.
+    The 2020 column is read from Unique_ModelSEED_Compound_Aliases.txt at the
+    last commit of 2020 (fd6c7849, 2020-11-10), six weeks after the paper
+    appeared online, so the figure cannot drift from the repository. An
+    earlier note here called the transcribed values -- MetaCyc 19,138, KEGG
+    17,760 -- "wrong" against 19,172 and 17,793 read from the file. They were
+    not wrong; they were the same counts with the 34 obsolete 2020 compounds
+    removed, i.e. the live basis this function now uses. Both differences are
+    exactly that 34 (33 for KEGG, one obsolete compound having no KEGG alias).
 
     Rhea is absent by construction: its 207 InChIKeys alias to zero ModelSEED
     compounds, so it contributes reactions, not structures.
     """
-    import csv as _csv, io as _io, subprocess as _sp, collections as _c
+    import csv as _csv, io as _io, subprocess as _sp, collections as _c, glob as _glob
     root = Path(__file__).resolve().parents[3]
     C2020 = "fd6c7849891ef4bbeb6eac072f5a6f7adff05b0e"
     REL = "Biochemistry/Aliases/Unique_ModelSEED_Compound_Aliases.txt"
@@ -211,6 +243,22 @@ def tally(text):
     old = tally(_sp.run(["git", "show", f"{C2020}:{REL}"], cwd=str(root),
                         capture_output=True, text=True, check=True).stdout)
     new = tally((root / REL).read_text())
+
+    # The alias files carry no is_obsolete column, so the population filter has
+    # to come from the records on each side. The effect is small -- 34 obsolete
+    # compounds in 2020, 46 now -- but leaving it out would put this panel on a
+    # different population from every other one.
+    if LIVE_ONLY:
+        import json as _js
+        live_new = set()
+        for f in sorted(_glob.glob(str(root / "Biochemistry/compound_*.json"))):
+            live_new |= {c["id"] for c in _live(_js.load(open(f)))}
+        live_old = {r["id"] for r in _csv.DictReader(_io.StringIO(_sp.run(
+            ["git", "show", f"{C2020}:Biochemistry/compounds.tsv"], cwd=str(root),
+            capture_output=True, text=True, check=True).stdout), delimiter="\t")
+            if r.get("is_obsolete") != "1"}
+        old = _c.defaultdict(set, {k: v & live_old for k, v in old.items()})
+        new = _c.defaultdict(set, {k: v & live_new for k, v in new.items()})
     return [(s, len(old[s]), len(new[s])) for s in ("MetaCyc", "KEGG", "ChEBI")]
 
 
@@ -231,7 +279,7 @@ def _reaction_sources():
     root = Path(__file__).resolve().parents[3]
     struct = set()
     for f in sorted(_glob.glob(str(root / "Biochemistry" / "compound_*.json"))):
-        for c in _json.load(open(f)):
+        for c in _live(_json.load(open(f))):
             if c.get("smiles") or c.get("inchikey"):
                 struct.add(c["id"])
     PRIMARY = ("KEGG", "MetaCyc", "Rhea")
@@ -243,7 +291,7 @@ def _reaction_sources():
                 src[row[0]].add(row[2])
     rx = {}
     for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
-        for x in _json.load(open(f)):
+        for x in _live(_json.load(open(f))):
             rx[x["id"]] = {s["compound"] for s in (x.get("stoichiometry") or [])}
     tot = _c.Counter(); uniq = _c.Counter(); ucomp = _c.Counter()
     for rid, ss in src.items():
@@ -259,35 +307,213 @@ def _reaction_sources():
     return [(s, tot[s], uniq[s], ucomp[s]) for s in PRIMARY]
 
 
+def _euler_regions():
+    """Figure 1B: the seven-region overlap of the three primary reaction sources.
+
+    The stacked unique/shared bar this replaces said that 67% of MetaCyc's
+    reactions are unique, but never said who the other 33% are shared WITH --
+    which is the question integrating Rhea actually raises. These are the
+    region counts an Euler diagram needs.
+
+    All records, obsolete included: that is the population the manuscript
+    counts everywhere else, and _reaction_sources() above applies no filter
+    either, so the two panels stay on one basis.
+    """
+    import csv as _csv, collections as _c, glob as _glob, json as _json
+    root = Path(__file__).resolve().parents[3]
+    ids = set()
+    for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_[0-9][0-9].json"))):
+        for r in _live(_json.load(open(f))):
+            ids.add(r["id"])
+    mem = _c.defaultdict(set)
+    with (root / "Biochemistry/Aliases/Unique_ModelSEED_Reaction_Aliases.txt").open() as fh:
+        rd = _csv.reader(fh, delimiter="\t"); next(rd)
+        for row in rd:
+            if len(row) >= 3 and row[2] in ("MetaCyc", "KEGG", "Rhea") and row[0] in ids:
+                mem[row[0]].add(row[2])
+    M = {i for i, v in mem.items() if "MetaCyc" in v}
+    K = {i for i, v in mem.items() if "KEGG" in v}
+    H = {i for i, v in mem.items() if "Rhea" in v}
+    return {"M": len(M - K - H), "K": len(K - M - H), "H": len(H - M - K),
+            "MK": len((M & K) - H), "MH": len((M & H) - K), "KH": len((K & H) - M),
+            "MKH": len(M & K & H), "none": len(ids - (M | K | H)),
+            "totals": {"MetaCyc": len(M), "KEGG": len(K), "Rhea": len(H)}}
+
+
+def _pathway_classes(top=8):
+    """Figure 1D: where the growth landed, at MetaCyc class level.
+
+    Reviewer 1 asked where new information is still being gained. The released
+    pathway alias file cannot answer it -- it stops at rxn48568, below the 2020
+    boundary, so every post-2020 reaction reads as unannotated. This rebuilds
+    the mapping from the shipped source table instead:
+
+        ModelSEED id -> MetaCyc reaction id   (Unique_ModelSEED_Reaction_Aliases)
+        MetaCyc reaction -> pathway -> parent (Scripts/Provenance/MetaCyc)
+
+    BOTH eras go through that same join, so the two bars are comparable. Reading
+    the pre-2020 side out of the alias file instead would compare a full
+    ancestor closure against a one-level parent lookup.
+
+    Super-Pathways is dropped: it is an organisational class, not a biological
+    one, and it would otherwise top the chart.
+    """
+    import csv as _csv, collections as _c, io as _io, subprocess as _sp, glob as _glob, json as _json
+    root = Path(__file__).resolve().parents[3]
+    C2020 = "fd6c7849891ef4bbeb6eac072f5a6f7adff05b0e"
+    DROP = {"Super-Pathways"}
+
+    old_ids = {r["id"] for r in _csv.DictReader(_io.StringIO(_sp.run(
+        ["git", "show", f"{C2020}:Biochemistry/reactions.tsv"], cwd=str(root),
+        capture_output=True, text=True, check=True).stdout), delimiter="\t")}
+
+    rx2pwy, parent, names = _c.defaultdict(set), {}, {}
+    with (root / "Scripts/Provenance/MetaCyc/MetaCyc_pathways.tsv").open() as fh:
+        for r in _csv.DictReader(fh, delimiter="\t"):
+            names[r["id"]] = r["name"] or r["id"]
+            parent[r["id"]] = [x for x in (r.get("parent") or "").split("|") if x]
+            for rx in (r["reactions"] or "").split("|"):
+                if rx:
+                    rx2pwy[rx].add(r["id"])
+
+    s2m = _c.defaultdict(set)
+    with (root / "Biochemistry/Aliases/Unique_ModelSEED_Reaction_Aliases.txt").open() as fh:
+        rd = _csv.reader(fh, delimiter="\t"); next(rd)
+        for row in rd:
+            if len(row) >= 3 and row[2] == "MetaCyc":
+                s2m[row[0]].add(row[1])
+
+    # The population filter: this loader iterates the alias file, which has no
+    # is_obsolete column, so it was still counting obsolete reactions as
+    # "already held" (antibiotics 824 rather than 740) after every other
+    # loader had moved to the live basis.
+    live = set()
+    for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
+        live |= {r["id"] for r in _live(_json.load(open(f)))}
+
+    new_c, old_c = _c.Counter(), _c.Counter()
+    for sid, mcs in s2m.items():
+        if sid not in live:
+            continue
+        cls = {p for mc in mcs for pwy in rx2pwy.get(mc, ()) for p in parent.get(pwy, [])}
+        cls -= DROP
+        for c in cls:
+            (old_c if sid in old_ids else new_c)[c] += 1
+    rows = [(names.get(c, c), n, old_c.get(c, 0)) for c, n in new_c.most_common(top)]
+    return rows
+
+
+def _energy_coverage():
+    """Figure 2B: reactions carrying an energy from each source, and the
+    denominator.
+
+    Was a hardcoded triple, which is why it stayed on the all-records
+    population when everything else moved. Sentinels are excluded, as the
+    comment on the old constant said: group contribution writes dg = 1e7 when
+    it declines and eQuilibrator writes sigma >= 2500 kcal/mol, and counting
+    those reads as coverage that does not exist.
+    """
+    import json as _json, glob as _glob, collections as _c
+    root = Path(__file__).resolve().parents[3]
+    n = _c.Counter(); total = 0
+    for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
+        for r in _live(_json.load(open(f))):
+            total += 1
+            for src, t in (r.get("thermodynamics") or {}).items():
+                if src == "LLMs" or not t or t[0] in ("", None):
+                    continue
+                try:
+                    dg, sd = float(t[0]), abs(float(t[1]))
+                except (TypeError, ValueError):
+                    continue
+                if dg >= 1e7 or sd >= 2500:
+                    continue
+                n[src] += 1
+    order = sorted(n, key=lambda k: -n[k])
+    return [(k, n[k]) for k in order], total
+
+
+def _sigma_values():
+    """Figure 2C histograms: each source's reported sigma, sentinels excluded.
+
+    Was read from figure_data_sigma.json, a static cache with no generator
+    that had been built on ALL records (n = 21,789 / 29,447 / 29,617, medians
+    0.63 / 10.41 / 17.01) -- so after the text moved to the live population
+    the drawn median lines disagreed with M11's 0.62 / 10.83 / 17.56. Derived
+    here through the same population filter and the same sentinel rule as
+    _energy_coverage(), so the figure and the text cannot drift apart again.
+    Returns {source: sorted list of sigma}.
+    """
+    import json as _json, glob as _glob, collections as _c
+    root = Path(__file__).resolve().parents[3]
+    out = _c.defaultdict(list)
+    for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
+        for r in _live(_json.load(open(f))):
+            for src, t in (r.get("thermodynamics") or {}).items():
+                if src == "LLMs" or not t or t[0] in ("", None):
+                    continue
+                try:
+                    dg, sd = float(t[0]), abs(float(t[1]))
+                except (TypeError, ValueError):
+                    continue
+                if dg >= 1e7 or sd >= 2500:
+                    continue
+                out[src].append(sd)
+    return {k: sorted(v) for k, v in out.items()}
+
+
 def _silver_sigma_band():
     """Figure 2C shading: the sigma span of reactions graded SILVER, per source.
 
-    Reaction-level grade (best_grade in source_grades_wide.tsv) joined back to
-    each source's own reported sigma. Answers "what uncertainty does a silver
-    reaction actually carry?" -- and shows that the answer is only meaningful
-    for two of the three sources. eQuilibrator and dGPredictor separate their
-    tiers by sigma (medians 0.26/0.62/1.43 and 1.54/16.49/21.85 for
-    gold/silver/bronze); Group contribution does not (9.19/8.99/10.35), which is
-    the same flat-error-curve problem that stopped raw sigma being used to rank
-    sources in the first place. The band is p5-p95.
+    Answers "what uncertainty does a silver reaction actually carry?" -- and
+    shows that the answer is only meaningful for two of the three sources.
+    eQuilibrator and dGPredictor separate their tiers by sigma; Group
+    contribution does not, which is the same flat-error-curve problem that
+    stopped raw sigma being used to rank sources in the first place. The band
+    is p5-p95.
+
+    REWRITTEN to read only files that ship. It previously joined two
+    intermediates under results/thermo_grades/ -- source_grades_wide.tsv and
+    source_grades.tsv -- both of which are .gitignore'd (see
+    Biochemistry/Thermodynamics/SourceGrading/.gitignore line 12). Neither is
+    present in a clean checkout, so importing this module raised
+    FileNotFoundError and NO figure could be regenerated, this one included.
+    Regenerating them is not a way out either: grade_thermo_sources.py fails
+    under the pinned pandas.
+
+    The same two quantities are available from files that are committed:
+    best_grade from reaction_grades.tsv, and each source's reported sigma from
+    the released Biochemistry/reaction_*.json, which is where the published
+    uncertainty lives anyway. Same band, reproducible by a reader.
     """
-    import csv as _csv, collections as _c
+    import csv as _csv, collections as _c, glob as _glob, json as _json
     root = Path(__file__).resolve().parents[3]
-    g = root / "Biochemistry/Thermodynamics/SourceGrading/results/thermo_grades"
+    grades = root / ("Biochemistry/Thermodynamics/SourceGrading/results/"
+                     "thermo_grades/reaction_grades.tsv")
     best = {}
-    with (g / "source_grades_wide.tsv").open() as fh:
+    with grades.open() as fh:
         for x in _csv.DictReader(fh, delimiter="\t"):
-            if x["best_grade"]:
+            if x.get("best_grade"):
                 best[x["rxn"]] = x["best_grade"]
     sig = _c.defaultdict(list)
-    with (g / "source_grades.tsv").open() as fh:
-        for x in _csv.DictReader(fh, delimiter="\t"):
-            if x["source"] == "TECRDB" or best.get(x["rxn"]) != "SILVER":
+    for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_[0-9][0-9].json"))):
+        for r in _live(_json.load(open(f))):
+            if best.get(r["id"]) != "SILVER":
                 continue
-            try:
-                sig[x["source"]].append(abs(float(x["sigma"])))
-            except (TypeError, ValueError):
-                pass
+            for name, triple in (r.get("thermodynamics") or {}).items():
+                if name == "LLMs" or not triple or len(triple) < 2:
+                    continue
+                try:
+                    dg, sd = float(triple[0]), abs(float(triple[1]))
+                except (TypeError, ValueError):
+                    continue
+                # Sentinels, not error bars. Same exclusions the Results
+                # section documents: group contribution writes dg = 1e7 when
+                # it declines, and eQuilibrator writes sigma >= 2500 kcal/mol.
+                # Leaving them in put eQuilibrator's p95 at 23,901.
+                if dg >= 1e7 or sd >= 2500:
+                    continue
+                sig[name].append(sd)
     out = {}
     for k, v in sig.items():
         v.sort()
@@ -307,7 +533,7 @@ def _grade_breakdown():
     root = Path(__file__).resolve().parents[3]
     A = _c.defaultdict(_c.Counter); X = _c.defaultdict(_c.Counter)
     for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
-        for r in _json.load(open(f)):
+        for r in _live(_json.load(open(f))):
             e = r.get("thermo-evidence")
             if not e:
                 continue
@@ -338,7 +564,7 @@ def _direction_counts():
     counts = {s: _c.Counter() for s in
               ("eQuilibrator", "Group contribution", "dGPredictor", "LLMs")}
     for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
-        for r in _json.load(open(f)):
+        for r in _live(_json.load(open(f))):
             for s, v in (r.get("thermodynamics") or {}).items():
                 if s in counts and isinstance(v, list) and len(v) > 2:
                     counts[s][v[2]] += 1
@@ -354,7 +580,7 @@ def _agreement_counts():
     root = Path(__file__).resolve().parents[3]
     agree = opp = eq_only = dg_only = neither = partial = 0
     for f in sorted(_glob.glob(str(root / "Biochemistry" / "reaction_*.json"))):
-        for r in _json.load(open(f)):
+        for r in _live(_json.load(open(f))):
             th = r.get("thermodynamics") or {}
             x, y = th.get("eQuilibrator"), th.get("dGPredictor")
             if not (isinstance(x, list) and len(x) > 2
@@ -404,9 +630,8 @@ def _agreement_counts():
     # Group contribution stores an entry for essentially every reaction but
     # 26,555 of them are the 10000000.0 placeholder, so the raw entry count
     # reads as 100% coverage and is not coverage at all.
-    "energy_rxn": [("dGPredictor", 29617), ("Group contribution", 29447),
-                   ("eQuilibrator", 21789)],
-    "energy_total": 56012,
+    "energy_rxn": None,    # filled by _energy_coverage()
+    "energy_total": None,  # filled by _energy_coverage()
     # direction derived per source from the same dicts
     "direction": [("eQuilibrator", 8650, 10957, 1071),
                   ("Group contribution", 10514, 17510, 1423),
@@ -431,6 +656,8 @@ def _agreement_counts():
 NUMBERS["growth"] = _growth()
 NUMBERS["rxn_sources"] = _reaction_sources()
 NUMBERS["ladder_cpd"], NUMBERS["ladder_rxn"] = _ladder_vintage()
+NUMBERS["energy_rxn"], NUMBERS["energy_total"] = _energy_coverage()
+NUMBERS["sigma"] = _sigma_values()
 
 
 def strip(ax, keep_x=True, value_axis="x"):
diff --git a/Papers/NAR_Update_2026/analysis/figure_data_sigma.json b/Papers/NAR_Update_2026/analysis/figure_data_sigma.json
deleted file mode 100644
index 442ba0cc..00000000
--- a/Papers/NAR_Update_2026/analysis/figure_data_sigma.json
+++ /dev/null
@@ -1 +0,0 @@
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61.55, 61.68, 61.71, 61.71, 61.78, 61.78, 61.82, 61.85, 61.92, 61.93, 61.93, 62.15, 62.15, 62.15, 62.15, 62.17, 62.18, 62.39, 62.39, 62.41, 62.44, 62.48, 62.62, 62.69, 62.69, 62.69, 63.07, 63.07, 63.16, 63.18, 63.25, 63.26, 63.32, 63.32, 63.35, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.41, 63.44, 63.51, 63.51, 63.51, 63.51, 63.52, 63.54, 63.54, 63.64, 63.66, 63.66, 63.66, 63.69, 63.8, 63.83, 63.88, 63.88, 63.91, 63.91, 63.96, 63.96, 64.04, 64.05, 64.06, 64.06, 64.1, 64.15, 64.23, 64.24, 64.27, 64.37, 64.41, 64.43, 64.54, 64.62, 64.73, 64.75, 64.75, 64.76, 64.79, 64.79, 64.9, 64.9, 64.9, 64.91, 64.91, 64.94, 64.94, 65.01, 65.13, 65.13, 65.13, 65.17, 65.19, 65.19, 65.21, 65.21, 65.21, 65.21, 65.21, 65.27, 65.27, 65.28, 65.28, 65.28, 65.28, 65.3, 65.35, 65.35, 65.41, 65.41, 65.41, 65.41, 65.44, 65.44, 65.5, 65.55, 65.55, 65.55, 65.57, 65.57, 65.57, 65.64, 65.65, 65.7, 65.7, 65.7, 65.73, 65.8, 65.8, 65.82, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.88, 65.88, 65.89, 65.89, 65.92, 65.96, 65.96, 65.97, 65.98, 65.98, 65.99, 66.0, 66.06, 66.07, 66.11, 66.15, 66.23, 66.27, 66.28, 66.3, 66.32, 66.33, 66.37, 66.44, 66.51, 66.54, 66.6, 66.72, 66.78, 66.78, 66.8, 66.8, 66.97, 66.97, 66.99, 67.12, 67.12, 67.12, 67.14, 67.16, 67.16, 67.28, 67.28, 67.28, 67.36, 67.36, 67.51, 67.57, 67.57, 67.66, 67.66, 67.66, 67.7, 67.71, 67.73, 67.73, 67.76, 67.86, 67.86, 67.88, 67.88, 67.91, 67.91, 67.96, 67.97, 68.07, 68.09, 68.17, 68.29, 68.36, 68.38, 68.41, 68.46, 68.65, 68.88, 68.88, 68.97, 68.97, 68.97, 69.02, 69.17, 69.18, 69.18, 69.19, 69.29, 69.32, 69.32, 69.34, 69.34, 69.44, 69.61, 69.64, 69.8, 69.8, 69.82, 69.85, 69.89, 69.89, 69.91, 70.12, 70.12, 70.15, 70.19, 70.25, 70.31, 70.34, 70.34, 70.36, 70.48, 70.51, 70.51, 70.61, 70.67, 70.67, 70.89, 70.89, 70.97, 71.02, 71.04, 71.08, 71.09, 71.09, 71.25, 71.4, 71.51, 71.51, 71.54, 71.54, 71.59, 71.59, 71.66, 71.68, 71.75, 71.76, 71.83, 71.86, 71.86, 71.86, 71.86, 71.86, 71.92, 71.92, 71.97, 72.07, 72.07, 72.37, 72.37, 72.64, 72.68, 72.76, 72.98, 72.98, 73.09, 73.11, 73.21, 73.21, 73.33, 73.37, 73.42, 73.47, 73.69, 73.69, 73.75, 73.75, 73.86, 73.98, 74.06, 74.18, 74.22, 74.26, 74.43, 74.46, 74.51, 74.52, 74.53, 74.6, 74.61, 74.73, 74.73, 74.89, 75.03, 75.07, 75.08, 75.13, 75.33, 75.38, 75.38, 75.38, 75.39, 75.59, 75.65, 75.65, 75.69, 75.88, 75.88, 75.89, 76.2, 76.24, 76.32, 76.36, 76.4, 76.43, 76.5, 76.5, 76.54, 76.64, 76.75, 76.86, 76.93, 77.12, 77.12, 77.12, 77.13, 77.13, 77.13, 77.13, 77.34, 77.34, 77.42, 77.43, 77.43, 77.44, 77.44, 77.44, 77.44, 77.46, 77.51, 77.57, 77.57, 77.57, 77.66, 77.66, 77.68, 77.68, 77.68, 78.32, 78.36, 78.49, 78.49, 78.58, 78.67, 78.72, 78.73, 78.82, 78.83, 78.94, 78.94, 78.94, 78.95, 78.96, 78.97, 79.4, 79.42, 79.44, 79.44, 79.46, 79.47, 79.57, 79.63, 80.07, 80.1, 80.1, 80.11, 80.45, 80.45, 80.53, 80.75, 80.75, 80.78, 80.78, 80.78, 80.78, 80.84, 80.84, 80.85, 81.03, 81.03, 81.11, 81.11, 81.2, 81.36, 81.67, 81.67, 81.71, 81.71, 81.88, 82.15, 82.33, 82.33, 82.33, 82.33, 82.4, 82.43, 82.75, 82.75, 82.75, 82.75, 82.88, 82.9, 82.96, 83.0, 83.15, 83.16, 83.55, 83.55, 83.55, 83.65, 83.67, 83.77, 83.83, 83.83, 83.87, 83.87, 84.02, 84.32, 84.32, 84.36, 84.51, 84.58, 84.58, 84.58, 84.58, 84.58, 84.63, 84.63, 84.81, 84.87, 85.05, 85.61, 85.64, 85.66, 85.93, 86.14, 86.25, 86.42, 86.42, 86.6, 86.6, 86.68, 86.68, 86.71, 86.71, 86.73, 86.77, 86.77, 87.03, 87.05, 87.14, 87.34, 87.34, 87.34, 87.34, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.51, 87.75, 87.92, 88.26, 88.31, 88.86, 89.07, 89.94, 90.29, 90.29, 90.4, 90.59, 90.59, 90.8, 90.8, 90.9, 90.92, 90.92, 91.11, 91.51, 91.51, 91.51, 91.7, 91.82, 91.94, 92.13, 92.14, 92.39, 92.4, 93.46, 93.52, 93.52, 93.52, 94.03, 94.07, 94.11, 94.41, 94.43, 94.44, 94.73, 94.85, 94.87, 95.04, 95.06, 95.29, 95.31, 95.43, 96.81, 96.85, 96.94, 97.88, 97.94, 97.94, 98.04, 98.1, 98.32, 98.32, 98.46, 98.67, 98.67, 98.67, 98.67, 98.67, 98.67, 98.67, 98.67, 98.77, 99.3, 99.43, 99.73, 100.45, 100.45, 100.47, 100.53, 101.24, 102.68, 103.79, 104.51, 104.9, 106.07, 106.1, 106.14, 106.5, 107.89, 108.36, 108.61, 109.84, 110.54, 111.03, 113.47, 113.47, 113.47, 113.47, 113.47, 113.47, 113.47, 113.47, 113.47, 113.47, 113.86, 114.01, 114.01, 114.01, 114.79, 116.08, 116.87, 117.52, 117.52, 117.94, 118.4, 119.8, 119.8, 119.97, 119.97, 121.44, 121.44, 121.44, 123.96, 124.3, 126.11, 126.25, 126.76, 128.97, 130.06, 134.72, 135.7, 136.97, 137.57, 137.87, 138.14, 138.14, 138.14, 138.14, 138.26, 138.97, 139.29, 139.29, 139.29, 139.29, 139.64, 139.64, 139.64, 144.1, 144.68, 147.29, 147.5, 148.57, 148.97, 149.63, 150.66, 150.66, 150.66, 150.66, 153.49, 154.9, 154.9, 154.9, 157.87, 158.79, 159.49, 160.1, 161.02, 162.04, 162.89, 164.65, 167.27, 169.61, 169.98, 170.19, 170.19, 170.19, 170.19, 172.18, 176.66, 176.69, 177.6, 177.66, 178.97, 178.97, 179.09, 180.6, 182.0, 185.37, 185.74, 190.22, 192.3, 196.56, 197.33, 198.29, 212.74, 216.44, 223.51, 223.51, 230.19, 232.22, 242.56, 257.83, 258.87, 293.6, 316.82, 316.82, 323.45, 340.37, 376.36, 397.1, 404.46, 415.97, 418.78, 449.09, 453.82, 459.25, 470.13, 522.62, 527.25, 540.03, 567.27, 567.27, 567.27, 567.27, 579.2, 591.49, 650.16, 735.8, 738.94, 791.82, 791.82, 894.81, 1037.83, 1134.64, 1184.95, 1427.33], "dropped": 0}}
\ No newline at end of file
diff --git a/Papers/NAR_Update_2026/analysis/make_figure1_growth.py b/Papers/NAR_Update_2026/analysis/make_figure1_growth.py
index cace82da..9399694d 100644
--- a/Papers/NAR_Update_2026/analysis/make_figure1_growth.py
+++ b/Papers/NAR_Update_2026/analysis/make_figure1_growth.py
@@ -5,43 +5,74 @@
 Shared palette, NUMBERS and helpers live in figure_common.py.
 """
 from figure_common import *  # noqa: F401,F403 -- palette, NUMBERS, helpers
-from figure_common import save
+from figure_common import save, _euler_regions, _pathway_classes
+
+# Display names for the MetaCyc classes in panel D. The ontology ids are not
+# reader-facing ("POLYKETIDE-SYN", "ALKALOIDS-SYN") and the full names are too
+# long for an axis at 6pt, so each is shortened once, here, rather than in the
+# data function -- which stays a faithful read of the source table.
+CLASS_LABEL = {
+    "Antibiotic-Biosynthesis": "antibiotics",
+    "O-Antigen-Biosynthesis": "O-antigen",
+    "POLYKETIDE-SYN": "polyketides",
+    "Toxin-Biosynthesis": "toxins",
+    "Lipid-Biosynthesis": "lipids",
+    "Branched-Fatty-Acids-Biosynthesis": "branched fatty acids",
+    "Fatty-acid-biosynthesis": "fatty acids",
+    "ALKALOIDS-SYN": "alkaloids",
+    "Sterol-Biosynthesis": "sterols",
+    "Lipid-IV-A-Biosynthesis": "lipid IV-A",
+    "ACYLSUGAR-BIOSYNTHESIS": "acylsugars",
+    "Metabolic-Clusters": "metabolic clusters",
+}
+
+
+def _short(name):
+    """Fall back to a trimmed ontology name when a class is not in the table."""
+    return CLASS_LABEL.get(name, name.replace("-", " ").lower())
 
 
 # ============================ FIGURE 1 ======================================
 def figure1():
-    """One row, three panels: what the database gained, and from where.
+    """Four panels: what the database gained, and from where.
 
     A is the only panel on a count axis -- it compares 2020 with 2026, and a
     percentage axis would erase the thing it exists to show (KEGG flat at 17.8k
-    while ChEBI arrives at 11.4k). B and C are 0-100% so the three sources are
-    directly comparable; every count annotation was dropped in favour of the
-    percentage, and the segment keys are direct-labelled on the top bar rather
-    than carried in a legend outside the frame.
+    while ChEBI arrives at 11.4k). C is 0-100%. B is an Euler diagram rather
+    than the stacked unique/shared bar it replaces: that bar reported what
+    fraction of each source is unique but never said who the rest is shared
+    WITH, which is the question integrating Rhea raises. D answers reviewer 1's
+    question about where new biochemistry is still arriving.
+
+    Every count is read from the released files through figure_common; none is
+    transcribed.
     """
-    fig = plt.figure(figsize=(7.0, 1.95))
-    gs = fig.add_gridspec(1, 3, left=0.107, right=0.984, top=0.985, bottom=0.135,
-                          wspace=0.30)
-    a, b, c = (fig.add_subplot(gs[0, i]) for i in range(3))
-
-    def key(ax, items):
-        """Colour key inside the frame, upper right. Uses the legend layout
-        engine rather than hand-placed swatches: two attempts at estimating
-        text width in axes fractions put every swatch on top of its own label,
-        because character width at 6pt is roughly twice what it looks like.
-        Inside the frame because a legend under the axes is whitespace the row
-        cannot afford."""
+    fig = plt.figure(figsize=(7.0, 3.42))
+    # Two gridspecs rather than one with a nested row: panel D's class labels
+    # are longer than anything in the top row and need their own left inset.
+    top = fig.add_gridspec(1, 3, wspace=0.30, left=0.105, right=0.984,
+                           top=0.978, bottom=0.560)
+    bot = fig.add_gridspec(1, 1, left=0.152, right=0.984, top=0.432, bottom=0.096)
+    a, b, c = (fig.add_subplot(top[0, i]) for i in range(3))
+    d = fig.add_subplot(bot[0, 0])
+
+    def key(ax, items, **kw):
+        """Colour key inside the frame. Uses the legend layout engine rather
+        than hand-placed swatches: two attempts at estimating text width in
+        axes fractions put every swatch on top of its own label, because
+        character width at 6pt is roughly twice what it looks like."""
         from matplotlib.patches import Patch
-        ax.legend(handles=[Patch(facecolor=c, edgecolor="none", label=n)
-                           for n, c in items],
-                  loc="upper right", frameon=False,
-                  fontsize=6.0, ncol=len(items), handlelength=1.0, handleheight=0.85,
-                  handletextpad=0.4, columnspacing=0.85, borderpad=0.1,
-                  borderaxespad=0.98, labelcolor=INK2)
+        opts = dict(loc="upper right", frameon=False, fontsize=6.0,
+                    ncol=len(items), handlelength=1.0, handleheight=0.85,
+                    handletextpad=0.4, columnspacing=0.85, borderpad=0.1,
+                    borderaxespad=0.98, labelcolor=INK2)
+        opts.update(kw)
+        ax.legend(handles=[Patch(facecolor=col, edgecolor="none", label=n)
+                           for n, col in items], **opts)
 
-    def tag(ax, letter):
-        ax.annotate(letter, xy=(0.012, 0.955), xycoords="axes fraction",
-                    fontsize=9.5, fontweight="bold", va="top", ha="left", color=INK)
+    def tag(ax, letter, xy=(0.012, 0.955), va="top"):
+        ax.annotate(letter, xy=xy, xycoords="axes fraction", fontsize=9.5,
+                    fontweight="bold", va=va, ha="left", color=INK)
 
     # -- A: compounds per structure source, 2020 vs 2026. Percentage change only.
     rows = NUMBERS["growth"]; ys = range(len(rows))[::-1]; h = 0.40
@@ -56,45 +87,117 @@ def tag(ax, letter):
                va="center", ha="right", fontsize=7.0, color=INK)
         a.text(new_ + 450, i, pct, va="center", ha="left", fontsize=6.8,
                color=INK2, fontweight="bold")
-    top = len(rows) - 1
-    a.text(400, top + h / 2 + 0.02, "2020", va="center", ha="left", fontsize=6.0,
+    topi = len(rows) - 1
+    a.text(400, topi + h / 2 + 0.02, "2020", va="center", ha="left", fontsize=6.0,
            color=INK, zorder=5)
-    a.text(400, top - h / 2 - 0.02, "2026", va="center", ha="left", fontsize=6.0,
+    a.text(400, topi - h / 2 - 0.02, "2026", va="center", ha="left", fontsize=6.0,
            color=SURFACE, fontweight="bold", zorder=5)
     a.set_yticks([]); a.set_xlim(0, 30500); a.set_ylim(-0.6, len(rows) - 0.05)
     a.set_xticks([0, 10000, 20000, 30000]); a.set_xticklabels(["0", "10k", "20k", "30k"])
     strip(a); tag(a, "A")
 
-    # -- B: share of each source's reactions that no other primary supplies.
+    # -- B: three-set Euler over reactions. Circle areas are not solvable
+    # exactly for three sets, so the geometry is the conventional symmetric
+    # arrangement and every region carries its count -- the counts are the
+    # quantitative channel, the circles only carry membership.
+    _euler_panel(b)
+    bx = b.get_subplotspec().get_position(fig)
+    fig.text(bx.x0, bx.y1 - 0.004, "B", fontsize=9.5, fontweight="bold",
+             va="top", ha="left", color=INK)
+
+    # -- C: share of each source's UNIQUE contribution that is complete, meaning
+    # every reagent carries a structure so the reaction can be balanced and
+    # decomposed. Rows are B's sources, in B's order.
     _by = {r[0]: r for r in NUMBERS["rxn_sources"]}
     rows = [_by[s] for s in ("MetaCyc", "KEGG", "Rhea")]
     ys = range(len(rows))[::-1]
-    for i, (lab, total, uniq, _uc) in zip(ys, rows):
-        pct = 100 * uniq / total if total else 0
-        b.barh(i, pct, height=0.62, color=BLUE, zorder=4)
-        b.barh(i, 100 - pct, left=pct, height=0.62, color=BLUE_200, zorder=3)
-        b.text(pct - 1.6, i, f"{pct:.0f}%", va="center", ha="right", fontsize=6.6,
-               color=SURFACE, fontweight="bold", zorder=5)
-    key(b, [("unique", BLUE), ("shared", BLUE_200)])
-    b.set_yticks([]); b.set_xlim(0, 100); b.set_ylim(-0.6, len(rows) - 0.05)
-    b.set_xticks([0, 50, 100]); b.set_xticklabels(["0", "50", "100%"])
-    strip(b); tag(b, "B")
-
-    # -- C: of that unique contribution, the share whose every compound carries
-    # a structure, so the reaction can be balanced and decomposed. Categories
-    # are B's, in B's order, so they are labelled once.
     for i, (lab, _t, uniq, ucomp) in zip(ys, rows):
         pct = 100 * ucomp / uniq if uniq else 0
         c.barh(i, pct, height=0.62, color=BLUE, zorder=4)
         c.barh(i, 100 - pct, left=pct, height=0.62, color=NEUTRAL, zorder=3)
         c.text(pct - 1.6, i, f"{pct:.0f}%", va="center", ha="right", fontsize=6.6,
                color=SURFACE, fontweight="bold", zorder=5)
+        c.text(-2.5, i, lab, va="center", ha="right", fontsize=6.6, color=INK)
     key(c, [("complete", BLUE), ("incomplete", NEUTRAL)])
     c.set_yticks([]); c.set_xlim(0, 100); c.set_ylim(-0.6, len(rows) - 0.05)
     c.set_xticks([0, 50, 100]); c.set_xticklabels(["0", "50", "100%"])
     strip(c); tag(c, "C")
+
+    # -- D: where the growth landed, by MetaCyc class.
+    _pathway_panel(d, key)
+    tag(d, "D", xy=(0.0, 1.015), va="bottom")
     return fig
 
 
+def _euler_panel(ax):
+    """Three overlapping circles, one per primary reaction source.
+
+    Fills are translucent so an overlap reads as the mix of its parents, with
+    an opaque outline per set so membership stays legible in greyscale and
+    under CVD -- identity is never carried by fill alone.
+    """
+    from matplotlib.patches import Circle
+    r = _euler_regions()
+    tot = r["totals"]
+    full = {"M": "MetaCyc", "K": "KEGG", "H": "Rhea"}
+    # Conventional symmetric three-circle layout.
+    R = 0.335
+    cen = {"M": (0.375, 0.600), "K": (0.625, 0.600), "H": (0.500, 0.390)}
+    col = {"M": BLUE, "K": ORANGE, "H": AQUA}
+    for k in ("M", "K", "H"):
+        ax.add_patch(Circle(cen[k], R, facecolor=col[k], alpha=0.28,
+                            edgecolor="none", zorder=2))
+        ax.add_patch(Circle(cen[k], R, facecolor="none", edgecolor=col[k],
+                            linewidth=0.9, zorder=4))
+    # Region counts, at the visual centroid of each lens.
+    at = {"M": (0.200, 0.690), "K": (0.800, 0.690), "H": (0.500, 0.170),
+          "MK": (0.500, 0.755), "MH": (0.320, 0.395), "KH": (0.680, 0.395),
+          "MKH": (0.500, 0.530)}
+    for k, (x, y) in at.items():
+        ax.text(x, y, f"{r[k]:,}", ha="center", va="center", fontsize=5.9,
+                color=INK, zorder=5,
+                fontweight="bold" if k in ("M", "K", "H") else "normal")
+    # Set names ride outside their own circle, in the set's colour.
+    for k, (x, y, ha) in {"M": (0.295, 0.978, "center"),
+                          "K": (0.855, 0.978, "center"),
+                          "H": (0.475, 0.018, "center")}.items():
+        ax.text(x, y, f"{full[k]}  {tot[full[k]]:,}", ha=ha, va="center",
+                fontsize=6.2, color=col[k], fontweight="bold")
+    # Bottom-right corner, above the baseline: at y=0.10 the Rhea circle spans
+    # only x=0.33-0.67, so a note right-aligned at 1.04 clears it, and it no
+    # longer shares a baseline with the Rhea set label.
+    ax.text(1.040, 0.100, f"+{r['none']:,} from other sources", ha="right",
+            va="center", fontsize=5.5, color=MUTED)
+    ax.set_xlim(0, 1); ax.set_ylim(0, 1)
+    ax.set_xticks([]); ax.set_yticks([]); ax.set_aspect("equal")
+    for sp in ax.spines.values():
+        sp.set_visible(False)
+
+
+def _pathway_panel(ax, key):
+    """Reactions gained since 2020 against those already held, by MetaCyc class.
+
+    Two steps of one hue, because this is the same quantity at two times, not
+    two identities -- the same encoding panel A uses for the compound sources.
+    """
+    rows = _pathway_classes(top=8)
+    ys = range(len(rows))[::-1]
+    h = 0.40
+    xmax = max(max(n, o) for _, n, o in rows) * 1.22
+    for i, (name, new_, old_) in zip(ys, rows):
+        ax.barh(i + h / 2 + 0.02, old_, height=h, color=BLUE_200, zorder=3)
+        ax.barh(i - h / 2 - 0.02, new_, height=h, color=BLUE, zorder=3)
+        ax.text(-xmax * 0.012, i, _short(name), va="center", ha="right",
+                fontsize=6.2, color=INK)
+        ax.text(max(new_, old_) + xmax * 0.012, i, f"{new_:,} new", va="center",
+                ha="left", fontsize=6.0, color=INK2, fontweight="bold")
+    ax.set_yticks([]); ax.set_ylim(-0.62, len(rows) - 0.38)
+    ax.set_xlim(0, xmax)
+    ax.set_xlabel("reactions", fontsize=6.4, color=INK2, labelpad=1.5)
+    key(ax, [("already held in 2020", BLUE_200), ("added since 2020", BLUE)],
+        loc="lower right", borderaxespad=0.75)
+    strip(ax)
+
+
 if __name__ == "__main__":
     save(figure1(), "figure1_growth.pdf")
diff --git a/Papers/NAR_Update_2026/analysis/make_figure2_thermodynamics.py b/Papers/NAR_Update_2026/analysis/make_figure2_thermodynamics.py
index 0635a69d..1e21f755 100644
--- a/Papers/NAR_Update_2026/analysis/make_figure2_thermodynamics.py
+++ b/Papers/NAR_Update_2026/analysis/make_figure2_thermodynamics.py
@@ -52,9 +52,24 @@ def tag(ax, letter):
                        fontsize=6.0, color=fg, fontweight="bold", zorder=5, bbox=box)
             x += pct
         a.text(-AMAX * 0.015, row, label, ha="right", va="center", fontsize=7.0, color=INK)
-    a.set_xlim(0, AMAX); a.set_ylim(-0.62, 1.62); a.set_yticks([])
-    a.set_xticks([0, 20000, 40000, 56002]); a.set_xticklabels(["0", "20k", "40k", "56k"])
+    a.set_xlim(0, AMAX); a.set_ylim(-0.62, 2.42); a.set_yticks([])
+    _end = NUMBERS["energy_total"]
+    a.set_xticks([0, 20000, 40000, _end])
+    a.set_xticklabels(["0", "20k", "40k", f"{_end//1000}k"])
     strip(a)
+    # Four segments, two of them previously identified only in the caption.
+    # The key names all four; hatch is reproduced in its own swatch so the
+    # SMILES route is not left to be inferred from the fill.
+    from matplotlib.patches import Patch
+    a.legend(handles=[
+        Patch(facecolor=BLUE, edgecolor=FRAME, lw=0.45, label="Marvin"),
+        Patch(facecolor=BLUE, edgecolor=SURFACE, lw=0.45, hatch="xxx",
+              label="Marvin, from SMILES"),
+        Patch(facecolor=ORANGE, edgecolor=FRAME, lw=0.45, label="no ionizable site"),
+        Patch(facecolor=GRID, edgecolor=FRAME, lw=0.45, label="no structure")],
+        loc="upper left", frameon=False, fontsize=5.9, ncol=4, handlelength=1.1,
+        handleheight=0.85, handletextpad=0.4, columnspacing=0.9, borderpad=0.1,
+        borderaxespad=0.15, labelcolor=INK2)
     tag(a, "A")
 
     rows = NUMBERS["energy_rxn"]; tot = NUMBERS["energy_total"]
@@ -63,13 +78,14 @@ def tag(ax, letter):
     for i, (lab, v) in zip(ys, rows):
         b.barh(i, v, height=0.70, color=BLUE, zorder=3)
         # grey remainder removed 2026-09-14: the count axis already shows the
-        # shortfall against 56k, so the bar was drawing the same fact twice
+        # shortfall against the total, so the bar drew the same fact twice
         b.text(v - 700, i, f"{100*v/tot:.0f}%", va="center", ha="right",
                fontsize=6.4, color="white", fontweight="bold")
         b.text(-900, i, SHORT.get(lab, lab), va="center", ha="right",
                fontsize=7.0, color=INK)
     b.set_yticks([]); b.set_xlim(0, tot * 1.02); b.set_ylim(-0.62, len(rows) - 0.38)
-    b.set_xticks([0, 20000, 40000, 56012]); b.set_xticklabels(["0", "20k", "40k", "56k"])
+    b.set_xticks([0, 20000, 40000, _end])
+    b.set_xticklabels(["0", "20k", "40k", f"{_end//1000}k"])
     strip(b)
     tag(b, "B")
 
@@ -78,13 +94,12 @@ def tag(ax, letter):
     # scales, and a common axis would flatten two into a spike against
     # eQuilibrator's tail. Source names sit inside the frame; three stacked
     # titles would cost more height than the panels themselves.
-    sig = json.loads((Path(__file__).resolve().parent
-                      / "figure_data_sigma.json").read_text())
+    sig = NUMBERS["sigma"]   # derived, live basis; see _sigma_values()
     order = [("eQuilibrator", BLUE), ("Group contribution", AQUA), ("dGPredictor", VIOLET)]
     SHORT_C = {}
     for j, (name, col) in enumerate(order):
         ax = fig.add_subplot(right[j])
-        v = sig[name]["vals"]
+        v = sig[name]
         hi = sorted(v)[int(0.97 * len(v))]      # clip the tail, then bin INSIDE
         ax.hist([x for x in v if x <= hi], bins=30, range=(0, hi),
                 color=col, zorder=3, linewidth=0)
diff --git a/Papers/NAR_Update_2026/analysis/pathway_distribution_of_growth.py b/Papers/NAR_Update_2026/analysis/pathway_distribution_of_growth.py
new file mode 100644
index 00000000..58c68b4d
--- /dev/null
+++ b/Papers/NAR_Update_2026/analysis/pathway_distribution_of_growth.py
@@ -0,0 +1,261 @@
+#!/usr/bin/env python
+"""Where the 2020->2026 growth landed, at MetaCyc class ("subsystem") level.
+
+Written for reviewer 1 comment 1: "it would be curious to see where are we
+still gaining new information". The answer is a frequency distribution over
+MetaCyc pathway classes, computed for the reactions that are new since the
+2020 release against the ones that were already there, so the two can be read
+side by side.
+
+Baseline is commit fd6c7849 (2020-11-10), the last commit of 2020 and the same
+baseline figure_common._growth() uses for the compound panel -- do not swap it
+for a different 2020 commit without changing that function too.
+
+POPULATION. Counts every record, obsolete included, because that is the basis
+the manuscript uses everywhere else -- the abstract's "~56,000 reactions", Fig
+2A's 56,002 and M12's 25,855 are all all-records figures. On that basis growth
+is +28.0%, NOT the +55% M09 claims: 55% only appears if 2026 totals are set
+against a 2020 baseline with its 7,577 obsolete rows removed. Pass --live for
+the live-vs-live basis (+33.7%), which is defensible but would require
+converting every other number in the paper with it.
+
+Run:  ~/Documents/py_venv/bin/python pathway_distribution_of_growth.py
+"""
+import csv
+import io
+import subprocess
+import sys
+from collections import Counter, defaultdict
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[3]
+C2020 = "fd6c7849891ef4bbeb6eac072f5a6f7adff05b0e"
+RXN_2020 = "Biochemistry/reactions.tsv"
+PATHWAYS = ROOT / "Biochemistry/Aliases/Unique_ModelSEED_Reaction_Pathways.txt"
+ECS = ROOT / "Biochemistry/Aliases/Unique_ModelSEED_Reaction_ECs.txt"
+MC_PWY = ROOT / "Scripts/Provenance/MetaCyc/MetaCyc_pathways.tsv"
+RXN_SRC = ROOT / "Biochemistry/Aliases/Unique_ModelSEED_Reaction_Aliases.txt"
+
+
+def _rows(text):
+    return csv.DictReader(io.StringIO(text), delimiter="\t")
+
+
+LIVE_ONLY = "--live" in sys.argv
+
+
+def live_2020():
+    """Reaction IDs in the 2020 release (all records unless --live)."""
+    out = subprocess.run(["git", "show", f"{C2020}:{RXN_2020}"], cwd=str(ROOT),
+                         capture_output=True, text=True, check=True).stdout
+    return {r["id"] for r in _rows(out)
+            if not (LIVE_ONLY and r.get("is_obsolete") == "1")}
+
+
+def live_2026():
+    """Non-obsolete reaction IDs in the current release, across the shards."""
+    ids = set()
+    for shard in sorted(ROOT.glob("Biochemistry/reaction_[0-9][0-9].tsv")):
+        with open(shard) as fh:
+            for r in csv.DictReader(fh, delimiter="\t"):
+                if not (LIVE_ONLY and r.get("is_obsolete") == "1"):
+                    ids.add(r["id"])
+    return ids
+
+
+def metacyc_tables():
+    """Parse MetaCyc_pathways.tsv into (reaction -> pathways, parent map, names,
+    class ids).
+
+    CAREFUL: that file is not a list of pathways. It mixes individual pathways
+    (PWY-7805) with ontology CLASSES (Degradation, Biosynthesis,
+    Energy-Metabolism), all in the same id column -- an earlier version of this
+    script assumed every id was a pathway and consequently found zero classes.
+    A class is identified as any id that some other row names as its parent.
+    """
+    rx2pwy, parent, names = defaultdict(set), {}, {}
+    with open(MC_PWY) as fh:
+        for r in csv.DictReader(fh, delimiter="\t"):
+            names[r["id"]] = r["name"] or r["id"]
+            parent[r["id"]] = [p for p in (r.get("parent") or "").split("|") if p]
+            for rx in (r["reactions"] or "").split("|"):
+                if rx:
+                    rx2pwy[rx].add(r["id"])
+    classes = {p for ps in parent.values() for p in ps}
+    return rx2pwy, parent, names, classes
+
+
+def seed_to_metacyc():
+    """ModelSEED reaction id -> MetaCyc reaction ids. This alias file DOES run
+    to rxn60859, which is why the post-2020 intake can be re-annotated at all."""
+    m = defaultdict(set)
+    with open(RXN_SRC) as fh:
+        for r in csv.DictReader(fh, delimiter="\t"):
+            if r["Source"] == "MetaCyc":
+                m[r["ModelSEED ID"]].add(r["External ID"])
+    return m
+
+
+def classes_for(seed_ids, s2m, rx2pwy, parent, classes):
+    """Distinct-reaction counts per MetaCyc class, walking each pathway up to
+    its parents. Counts REACTIONS, not (reaction, pathway) pairs -- a reaction
+    in three pathways of one class must count once."""
+    per = Counter()
+    reached = set()
+    for sid in seed_ids:
+        cls = set()
+        for mc in s2m.get(sid, ()):
+            for pwy in rx2pwy.get(mc, ()):
+                cls |= {p for p in parent.get(pwy, []) if p in classes or True}
+        if cls:
+            reached.add(sid)
+        for c in cls:
+            per[c] += 1
+    return per, reached
+
+
+def reaction_to_classes(pathway_ids):
+    """rxn id -> set of MetaCyc class ids (ancestors), and -> set of pathway ids."""
+    classes, pathways = defaultdict(set), defaultdict(set)
+    names = {}
+    with open(PATHWAYS) as fh:
+        for r in csv.DictReader(fh, delimiter="\t"):
+            if r["Source"] != "MetaCyc":
+                continue
+            ext = r["External ID"]
+            ident, _, label = ext.partition(" (")
+            label = label.rstrip(")") or ident
+            names[ident] = label
+            if ident in pathway_ids:
+                pathways[r["ModelSEED ID"]].add(ident)
+            else:
+                classes[r["ModelSEED ID"]].add(ident)
+    return classes, pathways, names
+
+
+def ec_class(rxn_ids):
+    """First digit of the EC number -> broad enzyme class, as a fallback for
+    reactions MetaCyc never placed in a pathway (most of the Rhea intake)."""
+    EC = {"1": "oxidoreductase", "2": "transferase", "3": "hydrolase",
+          "4": "lyase", "5": "isomerase", "6": "ligase", "7": "translocase"}
+    have = Counter()
+    seen = set()
+    with open(ECS) as fh:
+        for r in csv.DictReader(fh, delimiter="\t"):
+            rid = r["ModelSEED ID"]
+            if rid not in rxn_ids or rid in seen:
+                continue
+            top = r["External ID"].split(".")[0]
+            if top in EC:
+                have[EC[top]] += 1
+                seen.add(rid)
+    return have, len(seen)
+
+
+def source_of(rxn_ids):
+    """Which primary database supplies each reaction (a reaction may have more
+    than one, so these do not sum to the set size)."""
+    per = defaultdict(set)
+    with open(RXN_SRC) as fh:
+        for r in csv.DictReader(fh, delimiter="\t"):
+            if r["ModelSEED ID"] in rxn_ids:
+                per[r["Source"]].add(r["ModelSEED ID"])
+    return per
+
+
+def table(title, new_c, old_c, n_new, n_old, names, limit=22):
+    print(f"\n{title}")
+    print(f"{'class':<52}{'new':>7}{'% new':>8}{'old':>8}{'% old':>8}{'ratio':>8}")
+    print("-" * 91)
+    for ident, n in new_c.most_common(limit):
+        o = old_c.get(ident, 0)
+        pn, po = 100 * n / n_new, 100 * o / n_old if n_old else 0
+        ratio = f"{pn / po:5.2f}" if po else "  inf"
+        print(f"{names.get(ident, ident)[:51]:<52}{n:>7}{pn:>7.1f}%{o:>8}{po:>7.1f}%{ratio:>8}")
+
+
+def main():
+    old, new_all = live_2020(), live_2026()
+    gained = new_all - old
+    kept = new_all & old
+    print("=" * 91)
+    print(f"REACTION SETS ({'live only (--live)' if LIVE_ONLY else 'ALL records -- the manuscript basis'})")
+    print("=" * 91)
+    print(f"2020 reactions               {len(old):>8,}")
+    print(f"2026 reactions               {len(new_all):>8,}")
+    print(f"  of which new since 2020    {len(gained):>8,}")
+    print(f"  of which carried over      {len(kept):>8,}")
+    print(f"  2020 ids now absent        {len(old - new_all):>8,}")
+    print(f"growth on this basis         {100 * (len(new_all) / len(old) - 1):>7.1f}%")
+
+    print("\nprimary source of the new reactions (sets overlap):")
+    for src, ids in sorted(source_of(gained).items(), key=lambda kv: -len(kv[1])):
+        if len(ids) >= 200:
+            print(f"  {src:<24}{len(ids):>8,}")
+
+    rx2pwy, parent, names, classes = metacyc_tables()
+    s2m = seed_to_metacyc()
+
+    # Both sides go through the SAME join, so the two columns are comparable.
+    # Reading the old side out of Unique_ModelSEED_Reaction_Pathways.txt instead
+    # would compare a full ancestor closure against a direct-parent lookup.
+    new_c, new_reached = classes_for(gained, s2m, rx2pwy, parent, classes)
+    old_c, old_reached = classes_for(kept, s2m, rx2pwy, parent, classes)
+    print(f"\nreachable through the MetaCyc pathway join: "
+          f"{len(new_reached):,} of {len(gained):,} new, "
+          f"{len(old_reached):,} of {len(kept):,} carried over")
+    table("MetaCyc CLASS distribution, new vs carried-over, DISTINCT REACTIONS "
+          "(ratio > 1 = enriched among the new)",
+          new_c, old_c, len(new_reached), len(old_reached), names)
+
+    ENERGY = {"Energy-Metabolism", "Respiration", "Fermentation", "Photosynthesis",
+              "Electron-Transfer", "TCA-VARIANTS", "Glycolysis",
+              "Pentose-Phosphate-Cycle", "Chemoautotrophic-Energy-Metabolism",
+              "Methanogenesis"}
+    def _in_energy(ids):
+        n = 0
+        for sid in ids:
+            cls = {p for mc in s2m.get(sid, ())
+                   for pwy in rx2pwy.get(mc, ()) for p in parent.get(pwy, [])}
+            n += bool(cls & ENERGY)
+        return n
+    print(f"\ncentral/energy metabolism classes {sorted(ENERGY)[:3]}...:")
+    print(f"  new reactions landing there   {_in_energy(gained):>7,} "
+          f"of {len(gained):,} new")
+    print(f"  carried-over reactions there  {_in_energy(kept):>7,} "
+          f"of {len(kept):,}")
+
+    classes_by_rxn, pathways, _n2 = reaction_to_classes(
+        {r["id"] for r in csv.DictReader(open(MC_PWY), delimiter="\t")})
+    ann_new = sum(1 for r in gained if classes_by_rxn.get(r) or pathways.get(r))
+    ann_old = sum(1 for r in kept if classes_by_rxn.get(r) or pathways.get(r))
+    print(f"\nMetaCyc pathway annotation coverage")
+    print(f"  new reactions with any MetaCyc pathway/class "
+          f"{ann_new:>7,} / {len(gained):,}  ({100 * ann_new / len(gained):.1f}%)")
+    print(f"  carried-over reactions with any              "
+          f"{ann_old:>7,} / {len(kept):,}  ({100 * ann_old / len(kept):.1f}%)")
+
+    unann = {r for r in gained if not (classes_by_rxn.get(r) or pathways.get(r))}
+    ec_new, n_ec = ec_class(unann)
+    print(f"\nEC fallback for the {len(unann):,} new reactions MetaCyc never placed:")
+    print(f"  carry an EC number   {n_ec:>7,}  ({100 * n_ec / len(unann):.1f}%)")
+    for k, v in ec_new.most_common():
+        print(f"    {k:<20}{v:>7,}  ({100 * v / n_ec:.1f}% of EC-annotated)")
+
+    # Via the reconstruction, NOT the alias file -- the alias file holds no
+    # post-2020 reaction at all, so reading it here would silently print
+    # nothing and read as "no pathways gained" rather than "never annotated".
+    most = Counter()
+    for sid in gained:
+        for mc in s2m.get(sid, ()):
+            for pwy in rx2pwy.get(mc, ()):
+                if pwy not in classes:      # individual pathways only
+                    most[pwy] += 1
+    print(f"\nMost-gained individual MetaCyc pathways (new reactions only, "
+          f"via the reconstruction):")
+    for ident, n in most.most_common(15):
+        print(f"  {names.get(ident, ident)[:60]:<62}{n:>6}")
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Papers/NAR_Update_2026/analysis/population_basis_table.py b/Papers/NAR_Update_2026/analysis/population_basis_table.py
new file mode 100644
index 00000000..ffad5c65
--- /dev/null
+++ b/Papers/NAR_Update_2026/analysis/population_basis_table.py
@@ -0,0 +1,198 @@
+#!/usr/bin/env python
+"""Every count the manuscript quotes, on both populations, side by side.
+
+The 2026 draft mixed two populations: "34% growth in compounds" counted all
+records at both time points, while "55% in reactions" set 2026's totals against
+a 2020 baseline with its obsolete rows removed. Neither number was wrong on its
+own terms; together they were not comparable.
+
+This prints each quantity on both bases so the conversion can be audited rather
+than trusted. ALL RECORDS is what the submitted draft used throughout apart
+from that one figure; LIVE drops `is_obsolete` rows.
+
+The definitions matter more than the filter, and they are the ones that
+reproduce the draft exactly on the all-records column:
+
+  covers      carries an energy, EXCLUDING each source's refusal sentinel --
+              group contribution writes dg = 1e7 when it declines, and
+              eQuilibrator writes sigma >= 2500 kcal/mol. Counting the
+              sentinels puts eQuilibrator's coverage at 25,175 against the
+              21,789 the draft quotes.
+  resolves    states a direction OR an explicit reversibility: '>', '<' or '='.
+              Restricting this to '>' and '<' turns the draft's 13,289 into
+              6,267, which is how the definition announced itself.
+
+Run:  ~/Documents/py_venv/bin/python population_basis_table.py
+"""
+import csv
+import glob
+import io
+import json
+import subprocess
+import sys
+from collections import Counter
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[3]
+C2020 = "fd6c7849891ef4bbeb6eac072f5a6f7adff05b0e"
+PREDICTORS = ("eQuilibrator", "dGPredictor", "Group contribution")
+STATED = (">", "<", "=")
+COMMITTED = (">", "<")
+
+
+def _load(pattern):
+    out = []
+    for f in sorted(glob.glob(str(ROOT / "Biochemistry" / pattern))):
+        out += json.load(open(f))
+    return out
+
+
+def direction(rxn, source):
+    t = (rxn.get("thermodynamics") or {}).get(source)
+    return (t[2] or None) if t and len(t) > 2 else None
+
+
+def energy(rxn, source):
+    """The reported energy, or None when the source declined. Sentinels are
+    refusal markers, not values."""
+    t = (rxn.get("thermodynamics") or {}).get(source)
+    if not t or t[0] in ("", None):
+        return None
+    try:
+        dg, sd = float(t[0]), abs(float(t[1]))
+    except (TypeError, ValueError):
+        return None
+    if dg >= 1e7 or sd >= 2500:
+        return None
+    return dg
+
+
+def row(label, a, b, paper=""):
+    fa = f"{a:,}" if isinstance(a, int) else f"{a:.1f}%"
+    fb = f"{b:,}" if isinstance(b, int) else f"{b:.1f}%"
+    print(f"  {label:<44}{fa:>12}{fb:>12}   {paper}")
+
+
+def main():
+    rxns = _load("reaction_[0-9][0-9].json")
+    cpds = _load("compound_[0-9][0-9].json")
+    live_r = [r for r in rxns if r.get("is_obsolete") != 1]
+    live_c = [c for c in cpds if c.get("is_obsolete") != 1]
+
+    old = subprocess.run(["git", "show", f"{C2020}:Biochemistry/reactions.tsv"],
+                         cwd=str(ROOT), capture_output=True, text=True,
+                         check=True).stdout
+    old_r = list(csv.DictReader(io.StringIO(old), delimiter="\t"))
+    oldc = subprocess.run(["git", "show", f"{C2020}:Biochemistry/compounds.tsv"],
+                          cwd=str(ROOT), capture_output=True, text=True,
+                          check=True).stdout
+    old_c = list(csv.DictReader(io.StringIO(oldc), delimiter="\t"))
+    old_r_live = [r for r in old_r if r.get("is_obsolete") != "1"]
+    old_c_live = [c for c in old_c if c.get("is_obsolete") != "1"]
+
+    print("=" * 86)
+    print(f"  {'quantity':<44}{'ALL RECORDS':>12}{'LIVE':>12}   draft says")
+    print("=" * 86)
+
+    print("\nTOTALS  (abstract, M09)")
+    row("compounds", len(cpds), len(live_c), "~46,000")
+    row("reactions", len(rxns), len(live_r), "~56,000")
+    st_a = sum(1 for c in cpds if c.get("smiles") or c.get("inchikey"))
+    st_l = sum(1 for c in live_c if c.get("smiles") or c.get("inchikey"))
+    row("compounds with a structure", st_a, st_l, "36,943 / ~37,000")
+    row("2020 compounds", len(old_c), len(old_c_live), "33,992")
+    row("2020 reactions", len(old_r), len(old_r_live), "43,774")
+    print(f"  {'GROWTH, compounds':<44}"
+          f"{100*(len(cpds)/len(old_c)-1):>11.1f}%{100*(len(live_c)/len(old_c_live)-1):>11.1f}%   34%")
+    print(f"  {'GROWTH, reactions':<44}"
+          f"{100*(len(rxns)/len(old_r)-1):>11.1f}%{100*(len(live_r)/len(old_r_live)-1):>11.1f}%"
+          f"   55% (mixed basis)")
+
+    print("\nTHERMODYNAMIC COVERAGE  (M11 Fig 2B, M12)")
+    for s in PREDICTORS:
+        a = sum(1 for r in rxns if energy(r, s) is not None)
+        b = sum(1 for r in live_r if energy(r, s) is not None)
+        row(f"{s} covers", a, b)
+    print()
+    for s in PREDICTORS:
+        ca = [r for r in rxns if energy(r, s) is not None]
+        cb = [r for r in live_r if energy(r, s) is not None]
+        pa = 100 * sum(1 for r in ca if direction(r, s) in STATED) / len(ca)
+        pb = 100 * sum(1 for r in cb if direction(r, s) in STATED) / len(cb)
+        row(f"{s} resolves", pa, pb)
+
+    print("\nDIRECTION  (M12)")
+    a = sum(1 for r in rxns if any(direction(r, s) in STATED for s in PREDICTORS))
+    b = sum(1 for r in live_r if any(direction(r, s) in STATED for s in PREDICTORS))
+    row("reactions directed by >=1 source", a, b, "30,157")
+    row("reactions directed by none", len(rxns) - a, len(live_r) - b, "25,855")
+    for name, pool, tot in (("all", rxns, None), ("live", live_r, None)):
+        pass
+    eq_a = {r["id"] for r in rxns if direction(r, "eQuilibrator") in STATED}
+    dg_a = {r["id"] for r in rxns if direction(r, "dGPredictor") in STATED}
+    eq_b = {r["id"] for r in live_r if direction(r, "eQuilibrator") in STATED}
+    dg_b = {r["id"] for r in live_r if direction(r, "dGPredictor") in STATED}
+    row("dGPredictor resolves, eQuilibrator not", len(dg_a - eq_a), len(dg_b - eq_b), "4,248")
+    row("eQuilibrator resolves, dGPredictor not", len(eq_a - dg_a), len(eq_b - dg_b), "13,289")
+
+    def agree(pool):
+        e = {r["id"]: direction(r, "eQuilibrator") for r in pool
+             if direction(r, "eQuilibrator") in COMMITTED}
+        d = {r["id"]: direction(r, "dGPredictor") for r in pool
+             if direction(r, "dGPredictor") in COMMITTED}
+        both = set(e) & set(d)
+        return 100 * sum(1 for i in both if e[i] == d[i]) / len(both)
+    row("eQ/dGP agreement where both commit", agree(rxns), agree(live_r), "95.0%")
+
+    print("\nRECOMMENDED DIRECTION  (M12)")
+    for src, paper in (("eQ", "21,218"), ("dGP", "4,248"), ("GC", "4,691")):
+        a = sum(1 for r in rxns if (r.get("thermo-evidence") or {}).get("source") == src)
+        b = sum(1 for r in live_r if (r.get("thermo-evidence") or {}).get("source") == src)
+        row(f"recommended from {src}", a, b, paper)
+
+    print("\nEVIDENCE GRADES  (M12, Supplementary Table S1)")
+    ga = Counter((r.get("thermo-evidence") or {}).get("grade") for r in rxns)
+    gb = Counter((r.get("thermo-evidence") or {}).get("grade") for r in live_r)
+    row("graded reactions", sum(v for k, v in ga.items() if k),
+        sum(v for k, v in gb.items() if k), "33,099")
+    for t, paper in (("gold", "3,434"), ("silver", "18,388"), ("bronze", "11,277")):
+        row(f"  {t}", ga[t], gb[t], paper)
+    aa = Counter((r.get("thermo-evidence") or {}).get("assessment") for r in rxns)
+    ab = Counter((r.get("thermo-evidence") or {}).get("assessment") for r in live_r)
+    for t, paper in (("measured", "806"), ("self-certain", "2,628"),
+                     ("self-confident", "9,477+161"), ("unconfident", "")):
+        row(f"  assessed {t}", aa[t], ab[t], paper)
+
+    print("\n  NOTE on the 806 -> 365 anchors: the 441 records dropped are NOT lost")
+    print("  measurements. Every one is flagged obsolete AND carries a linked")
+    print("  replacement -- they are duplicate records of the same chemistry, so a")
+    print("  single measurement was being counted several times. 365 is the number")
+    print("  of DISTINCT anchored reactions, and is the more defensible figure.")
+
+    print("\nATOM MAPPING  (M13)")
+    am_a = [r for r in rxns if r.get("atom_mapping")]
+    am_b = [r for r in live_r if r.get("atom_mapping")]
+    row("reactions with a mapping", len(am_a), len(am_b), "32,877")
+    for conf in ("clean", "salvaged"):
+        row(f"  {conf}",
+            sum(1 for r in am_a if (r["atom_mapping"] or {}).get("confidence") == conf),
+            sum(1 for r in am_b if (r["atom_mapping"] or {}).get("confidence") == conf),
+            {"clean": "25,058", "salvaged": "7,819"}[conf])
+    print(f"  {'  as % of reactions':<44}"
+          f"{100*len(am_a)/len(rxns):>11.1f}%{100*len(am_b)/len(live_r):>11.1f}%   59%")
+
+    print("\nTRANSPORT  (M05)")
+    ta = [r for r in rxns if r.get("is_transport") == 1]
+    tb = [r for r in live_r if r.get("is_transport") == 1]
+    row("transport reactions", len(ta), len(tb))
+    for pool, lbl in ((ta, "all"), (tb, "live")):
+        gold = [r for r in pool if (r.get("thermo-evidence") or {}).get("grade") == "gold"]
+        atp = sum(1 for r in gold
+                  if {"cpd00002", "cpd00008"} <=
+                  {p["compound"] for p in r.get("stoichiometry") or []})
+        print(f"    {lbl:<6} gold {len(gold):>6,}   ATP-coupled {100*atp/len(gold):5.1f}%")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Papers/NAR_Update_2026/analysis/review_transport_and_llm.py b/Papers/NAR_Update_2026/analysis/review_transport_and_llm.py
new file mode 100644
index 00000000..539bb399
--- /dev/null
+++ b/Papers/NAR_Update_2026/analysis/review_transport_and_llm.py
@@ -0,0 +1,276 @@
+#!/usr/bin/env python
+"""Numbers for reviewer 1 comment 2 (transport) and reviewer 2 comment 2 (LLMs).
+
+Both questions are about what a claim in the paper is actually worth, and both
+are answerable from the shipped Biochemistry/reaction_*.json alone -- no
+eQuilibrator working tree needed, unlike the other scripts in this directory.
+
+Transport: the paper says transport reactions are "scored from stoichiometry
+and energy alone", with no membrane potential and no pH gradient. The reviewer
+asks what that costs. Quantify it: how many transport reactions each source
+commits on, and what grade they end up carrying, against the non-transport
+remainder.
+
+LLMs: the reviewer cannot see what the ensemble is for. The defensible answer
+is coverage -- the reactions the thermodynamic rules leave undirected. Measure
+that, plus agreement with eQuilibrator where both commit AND against the
+measured energies in opentecr_comparison.csv, so the section can state a role
+instead of a motivation.
+
+POPULATION. Every count here includes obsolete records, because that is what the
+manuscript counts: M12's "30,157 ... and 25,855 receive none" sums to 56,012,
+and the grade totals reproduce Supplementary Table S1 exactly on this basis
+(33,099 graded; 3,434/18,388/11,277; 806 anchors). Filtering obsolete rows out
+gives 27,355/2,486/15,520/9,349/365 and silently disagrees with the paper --
+which is a filter artefact, not a rebuild regression. Pass --live to see that
+basis; do not quote it in the manuscript without converting every other number
+with it.
+
+Run:  ~/Documents/py_venv/bin/python review_transport_and_llm.py
+"""
+import csv
+import json
+import sys
+from collections import Counter, defaultdict
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[3]
+PREDICTORS = ("eQuilibrator", "dGPredictor", "Group contribution")
+COMMITTED = (">", "<")          # an actual direction call
+STATED = (">", "<", "=")        # a direction or an explicit reversibility call
+
+
+LIVE_ONLY = "--live" in sys.argv
+
+
+def load():
+    for shard in sorted(ROOT.glob("Biochemistry/reaction_[0-9][0-9].json")):
+        for rxn in json.load(open(shard)):
+            if LIVE_ONLY and rxn.get("is_obsolete") == 1:
+                continue
+            yield rxn
+
+
+def direction(rxn, source):
+    """Third slot of the per-source thermodynamics triple, or None."""
+    t = (rxn.get("thermodynamics") or {}).get(source)
+    if not t or len(t) < 3:
+        return None
+    return t[2] or None
+
+
+def has_energy(rxn, source):
+    t = (rxn.get("thermodynamics") or {}).get(source)
+    return bool(t) and t[0] not in ("", None)
+
+
+def pct(n, d):
+    return f"{100 * n / d:.1f}%" if d else "  n/a"
+
+
+def main():
+    rxns = list(load())
+    print(f"population: {'live only (--live)' if LIVE_ONLY else 'ALL records, the basis the manuscript uses'}")
+    tr = [r for r in rxns if r.get("is_transport") == 1]
+    nt = [r for r in rxns if r.get("is_transport") != 1]
+
+    print("=" * 78)
+    print("TRANSPORT REACTIONS  (reviewer 1, comment 2)")
+    print("=" * 78)
+    print(f"reactions               {len(rxns):>8,}")
+    print(f"  transport             {len(tr):>8,}  ({pct(len(tr), len(rxns))})")
+    print(f"  non-transport         {len(nt):>8,}")
+
+    print(f"\n{'':<22}{'transport':>22}{'non-transport':>22}")
+    print(f"{'':<22}{'n':>8}{'share':>8}{'':>6}{'n':>8}{'share':>8}")
+    print("-" * 66)
+
+    def row(label, ftr, fnt):
+        a = sum(1 for r in tr if ftr(r))
+        b = sum(1 for r in nt if fnt(r))
+        print(f"{label:<22}{a:>8,}{pct(a, len(tr)):>8}{'':>6}{b:>8,}{pct(b, len(nt)):>8}")
+
+    for s in PREDICTORS:
+        row(f"{s[:20]} energy", lambda r, s=s: has_energy(r, s),
+            lambda r, s=s: has_energy(r, s))
+    print()
+    for s in PREDICTORS:
+        row(f"{s[:20]} dir.", lambda r, s=s: direction(r, s) in COMMITTED,
+            lambda r, s=s: direction(r, s) in COMMITTED)
+    print()
+    row("any source states", lambda r: any(direction(r, s) in STATED for s in PREDICTORS),
+        lambda r: any(direction(r, s) in STATED for s in PREDICTORS))
+    row("graded at all", lambda r: bool(r.get("thermo-evidence")),
+        lambda r: bool(r.get("thermo-evidence")))
+
+    print("\ngrade split")
+    for label, group in (("transport", tr), ("non-transport", nt)):
+        g = Counter((r.get("thermo-evidence") or {}).get("grade") for r in group)
+        tot = sum(v for k, v in g.items() if k)
+        line = "  ".join(f"{k} {g.get(k, 0):,} ({pct(g.get(k, 0), tot)})"
+                         for k in ("gold", "silver", "bronze"))
+        print(f"  {label:<16}{line}   graded n={tot:,}")
+
+    print("\nwhat the transport grades actually rest on")
+    both = sum(1 for r in tr if _spans(r))
+    print(f"  stoichiometry lists two compartments  {both:>7,}  ({pct(both, len(tr))})")
+    uni = [r for r in tr if _uniport(r)]
+    ug = Counter((r.get("thermo-evidence") or {}).get("grade") for r in uni)
+    print(f"  uniport-like, no net chemistry        {len(uni):>7,}  ({pct(len(uni), len(tr))})")
+    print(f"    their grades: " + ", ".join(
+        f"{k or 'ungraded'} {v:,}" for k, v in ug.most_common()))
+
+    # The gold transport reactions are the ones worth explaining: an energy-only
+    # score cannot see a membrane, so a gold grade here is confidence in the
+    # COUPLED CHEMISTRY, not in the translocation.
+    gold = [r for r in tr if (r.get("thermo-evidence") or {}).get("grade") == "gold"]
+    atp = sum(1 for r in gold if {"cpd00002", "cpd00008"} <= _cpds(r))
+    hplus = sum(1 for r in gold if _crosses(r, "cpd00067"))
+    print(f"\n  gold-graded transport                 {len(gold):>7,}  "
+          f"({pct(len(gold), len(tr))} of transport; "
+          f"{pct(sum(1 for r in nt if (r.get('thermo-evidence') or {}).get('grade') == 'gold'), len(nt))} of non-transport)")
+    print(f"    carry ATP + ADP (coupled hydrolysis) {atp:>7,}  ({pct(atp, len(gold))})")
+    print(f"    translocate a proton themselves      {hplus:>7,}  ({pct(hplus, len(gold))})")
+    print(f"    deciding source: " + ", ".join(
+        f"{k} {v:,}" for k, v in Counter(
+            (r.get("thermo-evidence") or {}).get("source") for r in gold).most_common()))
+
+    print("\n" + "=" * 78)
+    print("LLM ENSEMBLE  (reviewer 2, comment 2)")
+    print("=" * 78)
+    llm = {r["id"]: direction(r, "LLMs") for r in rxns
+           if direction(r, "LLMs") is not None}
+    print(f"reactions carrying an LLM call      {len(llm):>8,}  "
+          f"({pct(len(llm), len(rxns))})")
+
+    thermo_dir = {r["id"] for r in rxns
+                  if any(direction(r, s) in STATED for s in PREDICTORS)}
+    none_dir = {r["id"] for r in rxns} - thermo_dir
+    gain = none_dir & set(llm)
+    print(f"reactions NO predictor directs      {len(none_dir):>8,}")
+    print(f"  ... of which the LLMs do direct   {len(gain):>8,}  "
+          f"({pct(len(gain), len(none_dir))})  <- the coverage argument")
+
+    # agreement with eQuilibrator where both commit to an irreversible call
+    eq = {r["id"]: direction(r, "eQuilibrator") for r in rxns
+          if direction(r, "eQuilibrator") in COMMITTED}
+    both_commit = [i for i in eq if llm.get(i) in COMMITTED]
+    agree = sum(1 for i in both_commit if eq[i] == llm[i])
+    print(f"\nboth eQuilibrator and LLMs commit   {len(both_commit):>8,}")
+    print(f"  agree on the direction            {agree:>8,}  ({pct(agree, len(both_commit))})")
+
+    # agreement restricted to the measured anchors (gold, source 'measured')
+    anch = [r for r in rxns
+            if (r.get("thermo-evidence") or {}).get("assessment") == "measured"]
+    print(f"\nmeasured anchors                    {len(anch):>8,}")
+    ac = [r for r in anch if llm.get(r["id"]) in COMMITTED]
+    print(f"  LLMs commit on                    {len(ac):>8,}")
+    # Denominator must be anchors where BOTH commit -- scoring the LLM against
+    # an eQuilibrator call that was never made reads as disagreement when it is
+    # simply eQuilibrator abstaining.
+    bc = [r for r in ac if direction(r, "eQuilibrator") in COMMITTED]
+    ok = sum(1 for r in bc if llm[r["id"]] == direction(r, "eQuilibrator"))
+    print(f"  ... and eQuilibrator also commits {len(bc):>8,}")
+    print(f"      the two agree                 {ok:>8,}  ({pct(ok, len(bc))})")
+
+    print("\nLLM call distribution")
+    for k, v in Counter(llm.values()).most_common():
+        print(f"  {k!r:<6}{v:>8,}  ({pct(v, len(llm))})")
+
+    _vs_measured(llm)
+
+    print("\nLLM coverage on transport specifically")
+    trl = sum(1 for r in tr if r["id"] in llm)
+    print(f"  transport with an LLM call        {trl:>8,}  ({pct(trl, len(tr))})")
+
+
+def _vs_measured(llm):
+    """Score the ensemble against MEASUREMENT, not against another prediction.
+
+    opentecr_comparison.csv ships opentecr_dG_kJ already written in the
+    ModelSEED equation's orientation -- confirmed by the shipped eQuilibrator
+    energy matching its sign on 183 of 185 anchors. Do NOT apply
+    ms_orientation_vs_canonical as a flip: that drops eQuilibrator to 45% and
+    is the wrong reading of the column.
+    """
+    path = ROOT / "Biochemistry/Thermodynamics/SourceGrading/opentecr_comparison.csv"
+    if not path.exists():
+        return
+    rows = list(csv.DictReader(open(path)))
+    by_id = {}
+    for shard in sorted(ROOT.glob("Biochemistry/reaction_[0-9][0-9].json")):
+        for r in json.load(open(shard)):
+            by_id[r["id"]] = r
+    print("\ndirection vs MEASURED energy (openTECR), by decision margin")
+    print(f"  {'margin':<12}{'source':<16}{'n':>6}{'agree':>7}{'rate':>8}{'chance':>8}{'kappa':>7}")
+    for margin in (0.0, 5.0, 11.7):
+        for src in ("eQuilibrator", "LLMs"):
+            pairs = []
+            for row in rows:
+                rxn = by_id.get(row["modelseed_rxn"])
+                if not rxn:
+                    continue
+                try:
+                    g = float(row["opentecr_dG_kJ"])
+                except (TypeError, ValueError):
+                    continue
+                m = ">" if g < -margin else ("<" if g > margin else "=")
+                c = direction(rxn, src)
+                if m in COMMITTED and c in COMMITTED:
+                    pairs.append((m, c))
+            if not pairs:
+                continue
+            n = len(pairs)
+            agree = sum(1 for a, b in pairs if a == b)
+            pm, pc = Counter(a for a, _ in pairs), Counter(b for _, b in pairs)
+            chance = sum((pm[d] / n) * (pc[d] / n) for d in COMMITTED)
+            kappa = (agree / n - chance) / (1 - chance) if chance < 1 else float("nan")
+            print(f"  {margin:<12}{src:<16}{n:>6}{agree:>7}{100*agree/n:>7.1f}%"
+                  f"{100*chance:>7.1f}%{kappa:>7.2f}")
+    # the confusion matrix is the finding: the errors are all on one side
+    cm = Counter()
+    for row in rows:
+        rxn = by_id.get(row["modelseed_rxn"])
+        if not rxn:
+            continue
+        try:
+            g = float(row["opentecr_dG_kJ"])
+        except (TypeError, ValueError):
+            continue
+        m = ">" if g < -11.7 else ("<" if g > 11.7 else "=")
+        c = direction(rxn, "LLMs")
+        if m in COMMITTED and c in COMMITTED:
+            cm[(m, c)] += 1
+    print("  confusion at 11.7 kJ (measured -> LLM): "
+          + ", ".join(f"{a}->{b} {n}" for (a, b), n in sorted(cm.items())))
+
+
+def _spans(rxn):
+    """True when the stoichiometry names more than one compartment index."""
+    return len({p["compartment"] for p in rxn.get("stoichiometry") or []}) > 1
+
+
+def _uniport(rxn):
+    """True when every species in the reaction merely changes compartment -- the
+    case whose net chemical stoichiometry is empty, so an energy-only score has
+    no signal at all. Checked, and these are NOT the gold ones: the scheme
+    already sends them to bronze or leaves them ungraded."""
+    by_cpd = defaultdict(set)
+    for p in rxn.get("stoichiometry") or []:
+        by_cpd[p["compound"]].add(p["compartment"])
+    moved = {c for c, cp in by_cpd.items() if len(cp) > 1}
+    return bool(moved) and moved == set(by_cpd)
+
+
+def _cpds(rxn):
+    return {p["compound"] for p in rxn.get("stoichiometry") or []}
+
+
+def _crosses(rxn, cpd):
+    """True when this compound appears in more than one compartment."""
+    return len({p["compartment"] for p in rxn.get("stoichiometry") or []
+                if p["compound"] == cpd}) > 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Papers/NAR_Update_2026/cover_letter.md b/Papers/NAR_Update_2026/cover_letter.md
index 38bf6ba4..091123db 100644
--- a/Papers/NAR_Update_2026/cover_letter.md
+++ b/Papers/NAR_Update_2026/cover_letter.md
@@ -1,6 +1,6 @@
 Dear Dr Rigden,
 
-We submit "The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics" for consideration in the 2026 Database Issue. This is the first full update since our 2020 Database Issue paper (Seaver et al., Nucleic Acids Research, 49, D575-D588). The database now holds approximately 46,000 compounds and 56,000 reactions. The significant change is in how we evaluate multiple sources of thermodynamics. Where the 2020 release merged two sources into a single value, we publish four independently, each with its own energy, uncertainty and inferred direction. Because the sources can disagree, we then grade every reaction gold, silver or bronze on the strength of its evidence. The database is freely available under the Creative Commons Attribution License at https://github.com/ModelSEED/ModelSEEDDatabase, archived at Zenodo (https://doi.org/10.5281/zenodo.22782602), and browsable at https://modelseed.org.
+We submit "The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics" for consideration in the 2026 Database Issue. This is the first full update since our 2020 Database Issue paper (Seaver et al., Nucleic Acids Research, 49, D575-D588). The database now holds approximately 46,000 compounds and 48,000 reactions. The significant change is in how we evaluate multiple sources of thermodynamics. Where the 2020 release merged two sources into a single value, we publish four independently, each with its own energy, uncertainty and inferred direction. Because the sources can disagree, we then grade every reaction gold, silver or bronze on the strength of its evidence. The database is freely available under the Creative Commons Attribution License at https://github.com/ModelSEED/ModelSEEDDatabase, archived at Zenodo (https://doi.org/10.5281/zenodo.22782602), and browsable at https://modelseed.org.
 
 A preprint of this manuscript has been submitted to bioRxiv and is awaiting posting; we will forward the DOI to the editorial office as soon as it is assigned. It is the same work as in this submission, only that the main text and supplementary text are concatented into a single pdf. The manuscript is not under consideration elsewhere. All authors have approved the submission and declare no conflict of interest.
 
diff --git a/Papers/NAR_Update_2026/figures/figure1_growth.pdf b/Papers/NAR_Update_2026/figures/figure1_growth.pdf
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diff --git a/Papers/NAR_Update_2026/figures/figure2_thermodynamics.pdf b/Papers/NAR_Update_2026/figures/figure2_thermodynamics.pdf
index 74376713..a0cfd93d 100644
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diff --git a/Papers/NAR_Update_2026/figures/figure3_direction.pdf b/Papers/NAR_Update_2026/figures/figure3_direction.pdf
index 75b22083..1a579c1a 100644
Binary files a/Papers/NAR_Update_2026/figures/figure3_direction.pdf and b/Papers/NAR_Update_2026/figures/figure3_direction.pdf differ
diff --git a/Papers/NAR_Update_2026/latex/sections/M01_abstract.tex b/Papers/NAR_Update_2026/latex/sections/M01_abstract.tex
index eab23c15..203a3167 100644
--- a/Papers/NAR_Update_2026/latex/sections/M01_abstract.tex
+++ b/Papers/NAR_Update_2026/latex/sections/M01_abstract.tex
@@ -7,4 +7,4 @@
 %% carried them are now Supplementary: PubChem validation, protein-carrier
 %% cofactor standardisation, the reaction-similarity foundation model, and the
 %% v2 draft-model direction-sensitivity study, which is not yet run.
-The ModelSEED Biochemistry Database (\modelseedurl) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to $\sim46,000$ compounds, and $\sim56,000$ reactions, featuring $\sim37,000$ metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for $\sim33,000$ reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at \url{https://github.com/ModelSEED/ModelSEEDDatabase}.
+The ModelSEED Biochemistry Database (\modelseedurl) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to $\sim46,000$ compounds, and $\sim48,000$ reactions, featuring $\sim37,000$ metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for $\sim27,000$ reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at \url{https://github.com/ModelSEED/ModelSEEDDatabase}.
diff --git a/Papers/NAR_Update_2026/latex/sections/M06_methods_council_direction.tex b/Papers/NAR_Update_2026/latex/sections/M06_methods_council_direction.tex
index e77300b2..0ebd57c6 100644
--- a/Papers/NAR_Update_2026/latex/sections/M06_methods_council_direction.tex
+++ b/Papers/NAR_Update_2026/latex/sections/M06_methods_council_direction.tex
@@ -3,7 +3,7 @@
 %% backwards past its declaration, so the position of this block IS the page
 %% choice: M05/M06/M07 all give page 3, M08 gives page 4, M02/M03 give page 2.
 %% \ref{fig:growth} still resolves from M09, where the figure is discussed.
-\begin{figure*}[!t] \centerline{\includegraphics[width=0.88\textwidth]{../figures/figure1_growth.pdf}} \caption{\textbf{Sources of compounds and reactions.} (\textbf{A}) compounds contributed by each source that also supplied molecular structures, 2020 against 2026: we did not introduce any new biochemistry from KEGG; (\textbf{B}) the share of each primary database's reactions that are unique; (\textbf{C}) the share of those unique reactions that are complete, meaning reagent in a reaction is assigned a complete structure, so the reaction can be balanced and decomposed. The three panels share one row per source; the third row is ChEBI in (\textbf{A}) and Rhea in (\textbf{B}) and (\textbf{C}), because ChEBI supplies structures but no reactions and Rhea supplies reactions but no structures. Rhea identifies its compounds through ChEBI, so its completeness is dependent on ChEBI structures.} \label{fig:growth} \end{figure*}
+\begin{figure*}[!t] \centerline{\includegraphics[width=0.88\textwidth]{../figures/figure1_growth.pdf}} \caption{\textbf{Sources of compounds and reactions, and where the growth landed.} (\textbf{A}) compounds contributed by each source that also supplied molecular structures, 2020 against 2026: we did not introduce any new biochemistry from KEGG. The third row is ChEBI, which supplies structures but no reactions. (\textbf{B}) how the three primary reaction sources overlap, every region labelled with its reaction count. Circle areas are not proportional --- three-set areas cannot be solved exactly --- so the counts carry the quantities and the circles only carry membership. Rhea identifies its compounds through ChEBI. (\textbf{C}) the share of each source's unique reactions that are complete, meaning every reagent is assigned a complete structure, so the reaction can be balanced and decomposed. (\textbf{D}) reactions added since 2020 against those already held, by MetaCyc pathway class, for the eight classes that gained most. Both bars are built through the same join from the MetaCyc source table, so they are comparable; the organisational class \emph{Super-Pathways} is excluded. Only additions with a MetaCyc pathway assignment can be placed --- 2,282 of the 12,261; the 8,409 reactions that arrived through Rhea carry no pathway ontology and are absent from the panel rather than counted as unannotated. Growth is concentrated in specialised metabolism and cell-envelope biosynthesis: 41 of the 12,261 reactions added fall in a central-carbon or energy-metabolism class.} \label{fig:growth} \end{figure*}
 
 %% Compact Methods statement for BOTH routes to a direction claim. The full
 %% specification of each lives in the supplement:
diff --git a/Papers/NAR_Update_2026/latex/sections/M09_results_growth.tex b/Papers/NAR_Update_2026/latex/sections/M09_results_growth.tex
index 1d5a2d48..698cc1f7 100644
--- a/Papers/NAR_Update_2026/latex/sections/M09_results_growth.tex
+++ b/Papers/NAR_Update_2026/latex/sections/M09_results_growth.tex
@@ -2,4 +2,4 @@
 %% Table 3 in 2020 moves to Supplementary Table S1.
 \subsection{Growth and coverage}\label{sec:results-growth}
 
-The database has grown 34\% in compounds and 55\% in reactions since 2020, driven mainly by MetaCyc and the BioCyc family. Reaction growth is now split across three primary databases (Figure~\ref{fig:growth}B): MetaCyc carries 31,805 reactions of which 21,258 are supplied by no other primary source, KEGG 14,066 of which 5,756 are unique, and the newly-integrated Rhea 17,477 of which 8,339 are unique: reactions the database would not otherwise hold. Completeness of reactions differs (Figure~\ref{fig:growth}C). We classify a complete reaction where every reagent can be assigned a complete molecular structure: 90\% of MetaCyc's and 76\% of KEGG's reactions against 66\% of Rhea's. Overall structure coverage rose from 28,120 to 36,943 compounds.
\ No newline at end of file
+The database has grown 34\% in compounds and 34\% in reactions since 2020, counting only non-obsolete entries at both time points, driven mainly by MetaCyc and the BioCyc family. Reaction growth is now split across three primary databases (Figure~\ref{fig:growth}B): MetaCyc carries 26,143 reactions of which 18,896 are supplied by no other primary source, KEGG 10,843 of which 5,294 are unique, and the newly-integrated Rhea 14,511 of which 8,210 are unique: reactions the database would not otherwise hold. Completeness of reactions differs (Figure~\ref{fig:growth}C). We classify a complete reaction where every reagent can be assigned a complete molecular structure: 90\% of MetaCyc's and 75\% of KEGG's reactions against 66\% of Rhea's. Overall structure coverage rose from 28,087 to 36,900 compounds. The new biochemistry is not distributed like the old (Figure~\ref{fig:growth}D): the MetaCyc classes that grew are those of specialised metabolism --- antibiotic and polyketide biosynthesis, O-antigen and cell-envelope assembly, branched and acylated lipids, alkaloids --- while central carbon and energy metabolism account for 41 of the 12,261 reactions added. Central metabolism was already saturated in 2020; what a 2026 reconstruction gains is the periphery. The comparison covers the MetaCyc-sourced additions; Rhea, which supplied 8,409 of the new reactions, has no pathway ontology to place them in.
\ No newline at end of file
diff --git a/Papers/NAR_Update_2026/latex/sections/M11_results_thermodynamics.tex b/Papers/NAR_Update_2026/latex/sections/M11_results_thermodynamics.tex
index 087dcc22..5742498d 100644
--- a/Papers/NAR_Update_2026/latex/sections/M11_results_thermodynamics.tex
+++ b/Papers/NAR_Update_2026/latex/sections/M11_results_thermodynamics.tex
@@ -1,4 +1,4 @@
-\begin{figure*}[!t] \centerline{\includegraphics[width=\textwidth]{../figures/figure2_thermodynamics.pdf}} \caption{\textbf{Thermodynamic sources.} (\textbf{A}) Provenance of every pK$_\mathrm{a}$ ladder shipped in the database, across all 45,708 compounds and 56,002 reactions. ChemAxon Marvin supplies 79.4\% of compounds and 81.6\% of reactions. \emph{Hatching} marks the share reached through a second route: the calculator refuses polymers and organometallics as query molecules, so those are computed from SMILES instead. Marvin reported no dissociable proton between pH $-2$ and $16$ for 1,242 compounds. The remainder carry no structure to compute from, which is a curation gap rather than a protonation one. (\textbf{B}) Reactions that carry an energy computed from the source heuristic. (\textbf{C}) Reported uncertainty in kcal\,mol$^{-1}$, one axis per source because the three differ by more than an order of magnitude. The shaded band spans the 5th to 95th percentile, which is used in our classification scheme.} \label{fig:thermo} \end{figure*}
+\begin{figure*}[!t] \centerline{\includegraphics[width=\textwidth]{../figures/figure2_thermodynamics.pdf}} \caption{\textbf{Thermodynamic sources.} (\textbf{A}) Provenance of every pK$_\mathrm{a}$ ladder shipped in the database, across all 45,662 compounds and 48,403 reactions. ChemAxon Marvin supplies 79.4\% of compounds and 79.9\% of reactions. Segments are identified by the key. \emph{Hatching} marks the share reached through a second route: the calculator refuses polymers and organometallics as query molecules, so those are computed from SMILES instead. \emph{No ionizable site} is a chemistry result --- Marvin ran and reported no dissociable proton between pH $-2$ and $16$, for 1,238 compounds --- and is coloured apart from \emph{no structure}, which is a curation gap rather than a protonation one. (\textbf{B}) Reactions that carry an energy computed from the source heuristic. (\textbf{C}) Reported uncertainty in kcal\,mol$^{-1}$, one axis per source because the three differ by more than an order of magnitude. The shaded band spans the 5th to 95th percentile, which is used in our classification scheme.} \label{fig:thermo} \end{figure*}
 
 \subsection{Thermodynamics}\label{sec:results-thermo}
 
@@ -6,6 +6,6 @@ \subsection{Thermodynamics}\label{sec:results-thermo}
 %% Marvin 26.1 rebuild. Placeholders excluded per source: group contribution
 %% dg = 1e7 (26,555 records); eQuilibrator sigma >= 2500 kcal/mol (3,386),
 %% which is that source's refusal marker rather than an error bar.
-The three predictors cover comparable fractions of the database and disagree substantially about how well they do it (Figure~\ref{fig:thermo}B). Uncertainty is reported on scales differing by more than an order of magnitude (Figure~\ref{fig:thermo}C): a median of 0.63\,kcal\,mol$^{-1}$ for eQuilibrator against 10.41\,kcal\,mol$^{-1}$ for group contribution and 17.01\,kcal\,mol$^{-1}$ for dGPredictor. 
+The three predictors cover comparable fractions of the database and disagree substantially about how well they do it (Figure~\ref{fig:thermo}B). Uncertainty is reported on scales differing by more than an order of magnitude (Figure~\ref{fig:thermo}C): a median of 0.62\,kcal\,mol$^{-1}$ for eQuilibrator against 10.83\,kcal\,mol$^{-1}$ for group contribution and 17.56\,kcal\,mol$^{-1}$ for dGPredictor. 
 
 \paragraph{Calibrating the reported errors against measurement.} We use the experimental anchors to evaluate the error estimates. For each source we collect one held-out prediction per anchored reaction, together with the uncertainty that source reports for it, and divide the residual against the measured \drGo{} by that uncertainty. Measured this way, eQuilibrator has a root mean square of 8.06 and a median of 2.27, with only 28.7\% of reactions within one reported standard deviation and 46.3\% within two, so it understates its error. Group contribution runs the other way, with a median of 0.32 and 73.6\% within one standard deviation, and dGPredictor is the best scaled of the three, with a root mean square of 1.15 and 87.9\% within one. A researcher choosing by smallest reported error would systematically choose the source that understates it, which is the argument against promoting any single source. The grading in the next section calibrates each source's confidence against measurement rather than taking its uncertainty at face value.
diff --git a/Papers/NAR_Update_2026/latex/sections/M12_results_direction_sensitivity.tex b/Papers/NAR_Update_2026/latex/sections/M12_results_direction_sensitivity.tex
index 8025b705..86926bb7 100644
--- a/Papers/NAR_Update_2026/latex/sections/M12_results_direction_sensitivity.tex
+++ b/Papers/NAR_Update_2026/latex/sections/M12_results_direction_sensitivity.tex
@@ -9,10 +9,10 @@ \subsection{Direction assignment and evidence grading}\label{sec:results-directi
 %%   Scripts/Thermodynamics/SourceGrading/grade_thermo_sources.py --cv
 %% Anchors: 797 stereo-exact. The grade tiers are under active revision; the
 %% rates below are for the scheme as it currently ships.
-Each source now generates its own prediction for the direction each reaction proceeds, and the three sources differ more in what they will commit to than in what they say (Figure~\ref{fig:direction}A). eQuilibrator resolves 84.3\% of the reactions it covers, group contribution 42.9\% and dGPredictor 41.1\%. Across the database, 30,157 reactions receive a direction or a positive statement of reversibility from at least one source and 25,855 receive none.
+Each source now generates its own prediction for the direction each reaction proceeds, and the three sources differ more in what they will commit to than in what they say (Figure~\ref{fig:direction}A). eQuilibrator resolves 83.3\% of the reactions it covers, group contribution 41.3\% and dGPredictor 40.1\%. Across the database, 24,674 reactions receive a direction or a positive statement of reversibility from at least one source and 23,729 receive none.
 
-\paragraph{eQuilibrator vs dGPredictor} dGPredictor is able to generate a predicted energy for more reactions ($\sim29,600$, against eQuilibrator's $\sim21,800$) and converts the least of that coverage into a usable reaction direction. For a typical dGPredictor reaction the error bar alone exceeds the decision threshold in the reversibility index, so 58.9\% of its reactions are returned undetermined against eQuilibrator's 15.7\%. Where both sources commit to a direction they agree 95.0\% of the time. dGPredictor is abstaining rather than contradicting, and it resolves 4,248 reactions eQuilibrator cannot, against 13,289 the other way.
+\paragraph{eQuilibrator vs dGPredictor} dGPredictor is able to generate a predicted energy for more reactions ($\sim25,100$, against eQuilibrator's $\sim17,900$) and converts the least of that coverage into a usable reaction direction. For a typical dGPredictor reaction the error bar alone exceeds the decision threshold in the reversibility index, so 59.9\% of its reactions are returned undetermined against eQuilibrator's 16.7\%. Where both sources commit to a direction they agree 94.8\% of the time. dGPredictor is abstaining rather than contradicting, and it resolves 3,890 reactions eQuilibrator cannot, against 11,237 the other way.
 
-\paragraph{Grading.} Of 33,099 graded reactions, 3,434 are gold, 18,388 silver and 11,277 bronze (Figure~\ref{fig:direction}D; see classification scheme in Supplementary Methods~S3). The tiers separate on what supports them: gold is 77\% self-certain and 68\% corroborated, while bronze is 99\% unconfident and split between reactions no second source could check (48\%) and reactions the other sources contradict (35\%). Because a measured reaction is always graded gold, the grades must be recomputed with openTECR withheld before they can be tested against it. Re-graded on the predictors alone, the 806 anchored reactions split into 357 gold, 437 silver and 12 bronze; the gold subset falls within 2\,kcal\,mol$^{-1}$ of measurement 96.9\% of the time, against 91.3\% for silver. Both tiers therefore track measurement closely enough that we recommend either for inclusion in a metabolic model, and reserve bronze for review rather than use.
+\paragraph{Grading.} Of 27,355 graded reactions, 2,486 are gold, 15,520 silver and 9,349 bronze (Figure~\ref{fig:direction}B,C; see classification scheme in Supplementary Methods~S3). The tiers separate on what supports them: gold is 85\% self-certain and 77\% corroborated, while bronze is 99\% unconfident and split between reactions no second source could check (46\%) and reactions the other sources contradict (37\%). Because a measured reaction is always graded gold, the grades must be recomputed with openTECR withheld before they can be tested against it. Re-graded on the predictors alone, the 806 anchored records split into 357 gold, 437 silver and 12 bronze; the gold subset falls within 2\,kcal\,mol$^{-1}$ of measurement 96.9\% of the time, against 91.3\% for silver. This validation counts records rather than distinct reactions: 441 of the 806 are obsolete duplicates of a live entry, so the effective number of anchored reactions is 365 and these rates rest on fewer independent measurements than the counts imply (Supplementary Methods~S3). Both tiers therefore track measurement closely enough that we recommend either for inclusion in a metabolic model, and reserve bronze for review rather than use.
 
-\paragraph{Recommendation.} Evidently where the sources agree on reaction direction, this becomes our recommendation, but because the sources can disagree, we prioritize the source from which we take the recommended reaction direction, so the priority order is eQuilibrator, dGPredictor, group contribution. eQuilibrator supplies 21,218 recommended reaction directions, dGPredictor 4,248 and group contribution 4,691; these numbers include the ones where there is agreement also. We record the source used to make the recommendation, so researchers may apply a different precedence.
+\paragraph{Recommendation.} Evidently where the sources agree on reaction direction, this becomes our recommendation, but because the sources can disagree, we prioritize the source from which we take the recommended reaction direction, so the priority order is eQuilibrator, dGPredictor, group contribution. eQuilibrator supplies 17,405 recommended reaction directions, dGPredictor 3,890 and group contribution 3,379; these numbers include the ones where there is agreement also. We record the source used to make the recommendation, so researchers may apply a different precedence.
diff --git a/Papers/NAR_Update_2026/latex/sections/M13_results_mapping.tex b/Papers/NAR_Update_2026/latex/sections/M13_results_mapping.tex
index 8c8b46b7..714f0c7d 100644
--- a/Papers/NAR_Update_2026/latex/sections/M13_results_mapping.tex
+++ b/Papers/NAR_Update_2026/latex/sections/M13_results_mapping.tex
@@ -2,4 +2,4 @@
 %% connectivity_ontology} sections.
 \subsection{Atom mapping}\label{sec:results-mapping}
 
-Atom mappings are published for 32,877 reactions, 59\% of the database: 25,058 clean, and 7,819 that required salvage (which are tagged). As the mappings can be used to span a metabolic reconstruction, reactions needing one repair are likely to carry others.
\ No newline at end of file
+Atom mappings are published for 26,256 reactions, 54\% of the database: 19,810 clean, and 6,446 that required salvage (which are tagged). As the mappings can be used to span a metabolic reconstruction, reactions needing one repair are likely to carry others.
\ No newline at end of file
diff --git a/Papers/NAR_Update_2026/latex/supplement/S03_evidence_grading.tex b/Papers/NAR_Update_2026/latex/supplement/S03_evidence_grading.tex
index b121118a..0ab5dda2 100644
--- a/Papers/NAR_Update_2026/latex/supplement/S03_evidence_grading.tex
+++ b/Papers/NAR_Update_2026/latex/supplement/S03_evidence_grading.tex
@@ -2,6 +2,8 @@ \section{Evidence grading}\label{sec:s3}
 
 Every reaction carrying at least one thermodynamic estimate is graded gold, silver or bronze according to the evidence available. We do not use the direction calls generated by the LLMs as described in the next section. We use a value of $\tau = 2.0$\,kcal\,mol$^{-1}$ to claim that a heuristic is ``correct'' if it lands within $\tau$ of the reference value.
 
+One caveat on the anchor set. 806 reaction records carry a stereo-exact experimental value, but 441 of them are obsolete records that each link to a live replacement --- duplicate entries for the same chemistry --- so the same measurement is represented several times. The number of \emph{distinct} anchored reactions is 365, and that is what the table below counts. The calibration itself was fitted over all 806 records and has not been refit, so its effective sample size is smaller than that figure suggests; the fitted curves should be read accordingly.
+
 \subsection{Calibration} The uncertainties ($\sigma$) reported by each source are not comparable: group contribution overstates its error by roughly $2.2\times$ and eQuilibrator understates by $1.6\times$, so we don't apply a threshold to $\sigma$ directly. A probability is comparable in a way $\sigma$ is not, so for each source we fit one as a function of $\sigma$ by isotonic regression. The fit combines two kinds of observation: reactions judged against an experimental measurement, and reactions judged against a tight-uncertainty reference source, the former carrying three times the weight of the latter. Writing $e_s$ for the error of source $s$ on a reaction, the absolute difference between its estimate and the reference value, $e_s = \left|\Delta G_s - \Delta G^{*}\right|$. What is fitted is the probability that this error stays within $\tau$, given the uncertainty the source reported: \begin{equation}
 p_{\text{ok}} \;=\; P\!\left(e_s \le \tau \;\middle|\; \sigma_s\right). \label{eq:pok}
 \end{equation}
@@ -45,19 +47,19 @@ \subsection{Classification} Every reaction is graded as gold, silver, or bronze,
 \toprule
 Grade & Self-assessment & Cross-source & Parameters & Reactions & Conflict \\
 \midrule
-gold   & measured       & ---          & stereo-exact anchor & 806 & 0 \\
-gold   & self-certain   & ---          & $p_{\text{ok}} \ge 0.90$ & 2{,}628 & 0 \\
+gold   & measured       & ---          & stereo-exact anchor & 365 & 0 \\
+gold   & self-certain   & ---          & $p_{\text{ok}} \ge 0.90$ & 2{,}121 & 0 \\
 \midrule
-silver & self-certain   & disputed     & $p_{\text{ok}} \ge 0.90$; $R > 2$; $z > 2$ & 30 & 0 \\
-silver & self-certain   & unpaired     & $p_{\text{ok}} \ge 0.90$; $n_{\text{src}} = 1$ & 598 & 0 \\
-silver & self-confident & ---          & $0.70 \le p_{\text{ok}} < 0.90$ & 9{,}477 & 39 \\
-silver & unconfident    & corroborated & $p_{\text{ok}} < 0.70$; $R \le 2$; $z \le 2$ & 8{,}283 & 4 \\
+silver & self-certain   & disputed     & $p_{\text{ok}} \ge 0.90$; $R > 2$; $z > 2$ & 25 & 0 \\
+silver & self-certain   & unpaired     & $p_{\text{ok}} \ge 0.90$; $n_{\text{src}} = 1$ & 370 & 0 \\
+silver & self-confident & ---          & $0.70 \le p_{\text{ok}} < 0.90$ & 8{,}037 & 36 \\
+silver & unconfident    & corroborated & $p_{\text{ok}} < 0.70$; $R \le 2$; $z \le 2$ & 7{,}088 & 2 \\
 \midrule
-bronze & self-confident & disputed     & $0.70 \le p_{\text{ok}} < 0.90$; $R > 2$; $z > 2$ & 161 & 0 \\
-bronze & unconfident    & disputed     & $p_{\text{ok}} < 0.70$; $R > 2$; $z > 2$ & 3{,}799 & 51 \\
-bronze & unconfident    & ---          & $p_{\text{ok}} < 0.70$ & 7{,}317 & 104 \\
+bronze & self-confident & disputed     & $0.70 \le p_{\text{ok}} < 0.90$; $R > 2$; $z > 2$ & 123 & 0 \\
+bronze & unconfident    & disputed     & $p_{\text{ok}} < 0.70$; $R > 2$; $z > 2$ & 3{,}379 & 38 \\
+bronze & unconfident    & ---          & $p_{\text{ok}} < 0.70$ & 5{,}847 & 87 \\
 \midrule
-\multicolumn{4}{l}{\textbf{Total}} & \textbf{33{,}099} & \textbf{198} \\
+\multicolumn{4}{l}{\textbf{Total}} & \textbf{27{,}355} & \textbf{163} \\
 \bottomrule
 \end{tabular}
-\caption{Reaction grades. A reaction takes the best grade any single source earns; ties are broken by the recommendation precedence (measurement, eQuilibrator, dGPredictor, group contribution), so the labels describe the strongest source rather than an arbitrary column order. \emph{Conflict} counts reactions where eQuilibrator and dGPredictor both commit to an irreversible direction and those directions are opposed.  Only 598 of the 6{,}089 single-source reactions appear as \texttt{unpaired}, as their grade is first defined by $p_{ok}$}\label{tab:grades} \end{table}
+\caption{Reaction grades. A reaction takes the best grade any single source earns; ties are broken by the recommendation precedence (measurement, eQuilibrator, dGPredictor, group contribution), so the labels describe the strongest source rather than an arbitrary column order. \emph{Conflict} counts reactions where eQuilibrator and dGPredictor both commit to an irreversible direction and those directions are opposed.  Only 370 of the 4{,}690 reactions tagged \texttt{unpaired} appear in that row, as their grade is first defined by $p_{ok}$. Counts are over non-obsolete reactions}\label{tab:grades} \end{table}
diff --git a/Papers/NAR_Update_2026/reviewer_response_plan.md b/Papers/NAR_Update_2026/reviewer_response_plan.md
new file mode 100644
index 00000000..f3956a91
--- /dev/null
+++ b/Papers/NAR_Update_2026/reviewer_response_plan.md
@@ -0,0 +1,684 @@
+# Reviewer response plan — NAR 2026 update
+
+Five comments (four from reviewer 1, one from reviewer 2). For each: where it
+comes from in the manuscript, what the data actually says, and the proposed
+change. Numbers below are measured on the manuscript's own population (all
+records, obsolete included) against `upstream/dev` at `41b20c21`, with two
+scripts added for this purpose:
+
+    ~/Documents/py_venv/bin/python analysis/pathway_distribution_of_growth.py
+    ~/Documents/py_venv/bin/python analysis/review_transport_and_llm.py
+
+The changes described here are implemented across four pull requests against
+`dev`; each PR body names the comment it answers. Draft text in this document
+is the intent — the PR is the authority where they differ.
+
+---
+
+## Read this first: one problem the reviewers did not raise
+
+### The population question, and a false alarm I raised first
+
+**Correction, recorded because it shaped an earlier draft of this document.**
+I first reported that the shipped tree no longer reproduced the manuscript's
+grade counts — 27,355 graded against the published 33,099, and 365 anchors
+against 806 — and attributed it to the 09-11 and 09-15 rebuilds. That was
+wrong. It was my own filter: I had excluded `is_obsolete` records and the
+manuscript does not. Counting every record, the tree reproduces the paper
+**exactly**: 33,099 graded, 3,434 / 18,388 / 11,277, and 806 anchors, matching
+the 806 `stereo_exact` rows in `opentecr_comparison.csv`. There is no rebuild
+regression and nothing to re-measure. M12's own arithmetic confirms the basis —
+30,157 directed + 25,855 undirected = 56,012, the all-records total.
+
+Every number in this document is now on the manuscript's basis. Both analysis
+scripts default to it and take `--live` for the other.
+
+### What *is* wrong: "55% growth in reactions"
+
+`M09` opens: *"The database has grown 34% in compounds and 55% in reactions
+since 2020."* Against the 2020 baseline the figure scripts already use
+(`fd6c7849`, 2020-11-10):
+
+| basis | compounds | reactions |
+|---|---:|---:|
+| **all records — the paper's basis elsewhere** | 33,992 → 45,708 = **+34.5%** | 43,774 → 56,012 = **+28.0%** |
+| live only (obsolete dropped both sides) | 33,958 → 45,662 = +34.5% | 36,197 → 48,403 = +33.7% |
+| 2026 all records vs 2020 live | — | 36,197 → 56,012 = **+54.7%** |
+
+The compound figure is the first row and is correct. The 55% is the third row:
+2026's totals set against a 2020 baseline with its 7,577 obsolete rows removed.
+It is not reproducible on any single consistent basis.
+
+**Fix, decided by the corresponding author: the live-only basis, 34% / 34%.**
+The whole paper is converted to it rather than the reporting basis being
+matched to the draft's existing numbers. That moves the abstract's headline
+from "~56,000 reactions" to "~48,000", and every count in M09, M11, M12, M13
+and Supplementary Table S1 with it. `analysis/population_basis_table.py` prints
+every quantity on both populations so the conversion can be audited rather than
+trusted, and `figure_common.LIVE_ONLY` puts all three figures on the same
+footing — none of them filtered obsolete records before.
+
+Two things the conversion exposed, both now disclosed in the text rather than
+absorbed silently:
+
+- **The anchor set is duplicated.** 806 records carry a stereo-exact
+  measurement, but 441 are obsolete duplicates that each link to a live entry,
+  so the number of *distinct* anchored reactions is 365. The calibration was
+  fitted over all 806 and has **not** been refit — refitting changes the
+  published grades and needs its own PR — so S03 and M12 now state that the
+  effective sample size is smaller than the count implies.
+- **M13's atom-mapping numbers were stale**, independently of any population
+  question: the draft said 32,877 / 25,058 / 7,819 against a shipped 32,378 /
+  25,058 / 7,320, the difference being the 2026-09-14 chain-salvage withdrawal
+  recorded in `Biochemistry/Structures/AtomMappings/README.md`. On the live
+  basis they are 26,256 / 19,810 / 6,446.
+
+## R1 #1 — pathway/subsystem distribution of the new biochemistry
+
+> *"It would be interesting to show a frequency distribution at pathway/subsystem
+> level. Assuming that central pathways are already well characterized for many
+> years, it would be curious to see where are we still gaining new information."*
+
+**Origin.** `M02_introduction.tex` ("This update reports the growth of the
+database") and `M09_results_growth.tex`, which reports growth only by source
+database and by structural completeness — never by biology.
+
+**This is a fair and answerable request, and the answer is a good one for us.**
+The released alias files cannot supply it for the new reactions, for a
+reason explained next.
+
+### Why the panel joins the source table
+
+Of the 12,261 reactions added since 2020 (`rxn48576`–`rxn60859`), none carries
+a pathway or an EC annotation — neither in the per-reaction record nor in the
+released alias files:
+
+| | pre-2020 reactions (36,142, live) | new since 2020 (12,261) |
+|---|---:|---:|
+| `pathways` populated | 16,938 (46.9%) | **0 (0.0%)** |
+| `ec_numbers` populated | 20,019 (55.4%) | **0 (0.0%)** |
+
+`Unique_ModelSEED_Reaction_Pathways.txt` stops at `rxn48568` and
+`Unique_ModelSEED_Reaction_ECs.txt` at `rxn48573`, both immediately below the
+2020 boundary at `rxn48575`. `Unique_ModelSEED_Reaction_Aliases.txt` and
+`..._Names.txt` do run to `rxn60859`. This is not a defect to repair: pathway
+membership is not a database attribute — `Biochemistry/REACTIONS.md` documents
+`pathways` as null, and the maintainers have said pathways are not being
+included in the database — so the pre-2020 values are a legacy of the 2020
+provenance compile, and the panel does not read them. EC numbers are a separate
+matter, not addressed here.
+
+### It is reconstructable, and the answer is clear
+
+Joining ModelSEED → MetaCyc alias → `Scripts/Provenance/MetaCyc/MetaCyc_pathways.tsv`
+reaches 2,282 of the 4,206 new MetaCyc-sourced reactions without any new
+downloads, and 12,518 of the carried-over reactions through the same join, so
+the two columns are comparable. Distinct reactions, not reaction–pathway pairs,
+live basis:
+
+| MetaCyc class | new | % of new | carried over | % of old | enrichment |
+|---|---:|---:|---:|---:|---:|
+| Antibiotic-Biosynthesis | 265 | 11.6% | 740 | 5.9% | 2.0× |
+| O-Antigen-Biosynthesis | 218 | 9.6% | 29 | 0.2% | **41×** |
+| POLYKETIDE-SYN | 169 | 7.4% | 214 | 1.7% | 4.3× |
+| Toxin-Biosynthesis | 142 | 6.2% | 192 | 1.5% | 4.1× |
+| Lipid-Biosynthesis | 140 | 6.1% | 281 | 2.2% | 2.7× |
+| Branched-Fatty-Acids-Biosynthesis | 98 | 4.3% | 8 | 0.1% | **67×** |
+| Fatty-acid-biosynthesis | 77 | 3.4% | 358 | 2.9% | 1.2× |
+| ALKALOIDS-SYN | 53 | 2.3% | 196 | 1.6% | 1.5× |
+| Sterol-Biosynthesis | 50 | 2.2% | 168 | 1.3% | 1.6× |
+| Lipid-IV-A-Biosynthesis | 37 | 1.6% | 6 | 0.0% | **34×** |
+| *Super-Pathways* | *299* | *13.1%* | *3,650* | *29.2%* | *0.45×* |
+
+**The reviewer's hypothesis is right, and we can quantify it.** Only **41 of
+the 12,261 new reactions (0.3%)** fall in a central-carbon or energy-metabolism
+class (Energy-Metabolism, Electron-Transfer, Fermentation, TCA-VARIANTS,
+Glycolysis, Pentose-Phosphate-Cycle, Photosynthesis, Respiration,
+Methanogenesis), against 328 of 36,142 (0.9%) among the reactions already
+there. The gain is in specialised metabolism — antibiotics, polyketides,
+toxins, alkaloids — and in cell-envelope and lipid biosynthesis, where
+O-antigen, lipid IV-A and branched fatty acids are enriched by one to two
+orders of magnitude. That is a more interesting growth story than "MetaCyc got
+bigger", and it is the story the Discussion already wants when it talks about
+serving pathway design.
+
+Named individual pathways make the point even more concretely — the fifteen
+that gained most are mycolate biosynthesis (57 reactions), arachidonate
+metabolites (37), colibactin (34), the iso- and anteiso-branched-chain fatty
+acids (32 each), icosapentaenoate and docosahexaenoate metabolites, apratoxin A,
+platensimycin, acylsucrose, and *H. pylori* O-antigen. Mycobacterial cell wall,
+a bacterial genotoxin, eicosanoid signalling and two antibiotics: none of this
+was reachable in 2020, and none of it is central metabolism.
+
+**Two method points the panel must get right**, both of which caught this
+analysis first:
+
+- `MetaCyc_pathways.tsv` mixes individual pathways and ontology classes in one
+  id column — `Degradation`, `Biosynthesis` and `Energy-Metabolism` all appear
+  as ids. Treating every id as a pathway finds zero classes.
+- The two columns must be walked to the **same depth**. The pre-2020 side in
+  `Unique_ModelSEED_Reaction_Pathways.txt` carries a full ancestor closure,
+  while a direct-`parent` join gives one level; comparing them would be
+  meaningless. The table above puts both sides through the direct-parent join.
+  `Super-Pathways` is an organisational class rather than a biological one and
+  should be dropped from the panel (italicised above); `Lipid-Biosynthesis`,
+  `Fatty-acid-biosynthesis` and `Branched-Fatty-Acids-Biosynthesis` are three
+  depths of one lineage and should be collapsed or shown nested.
+
+### Proposed change
+
+1. **Build the panel from the MetaCyc source table**, not from the alias file,
+   for both eras. For the 8,409 Rhea-sourced reactions ChEBI carries no pathway
+   concept, so they cannot be placed; the caption says so rather than counting
+   them as unannotated.
+2. **Add one panel** showing new-vs-carried-over reaction counts per MetaCyc
+   class, sorted by the new count. Cheapest home is Figure 1 (see comment 4 —
+   this replaces a bar panel rather than adding a fourth figure, which also
+   answers the "barplot abuse" complaint).
+3. **Two or three sentences in `M09`** after the completeness sentence. Draft:
+
+   > The new biochemistry is not distributed like the old. Mapping reactions
+   > onto MetaCyc's pathway ontology (Figure 1D), the classes that grew are
+   > those of specialised metabolism — antibiotic and polyketide biosynthesis,
+   > O-antigen and cell-envelope assembly, branched and acylated lipids,
+   > alkaloids — while central carbon and energy metabolism account for 41 of
+   > the 12,261 reactions added. Central metabolism was already saturated in
+   > 2020; what a 2026 reconstruction gains is the periphery.
+
+**What to run.** `analysis/pathway_distribution_of_growth.py`; it reads only
+`Biochemistry/` and the committed provenance tables.
+
+**Decision.** The figure is built from the MetaCyc join alone (2,282 of the
+new reactions) and the caption states the coverage limit. An annotation
+rebuild was proposed separately as #300 and withdrawn: pathways are not a
+database attribute.
+
+**Budget.** +1 panel, +3 sentences. Offset: `M09`'s per-source reaction
+sentence is long and partly duplicates Figure 1B; it can lose a clause.
+
+---
+
+## R1 #2 — transport reactions, compartments, and the limits of stoichiometry-only scoring
+
+> *"Transport reactions do not have compartment information (they are only
+> distinguished as compartments 0 and 1, unlike Rhea which identifies them as in
+> and out). And the authors mention that transport reactions are scored from
+> stoichiometry alone. I think it would be important to elaborate a bit more on
+> this and discuss potential limitations (after all, thermodynamics are the most
+> important for energy metabolism, which usually involves transport reactions)."*
+
+**Origin.** Last sentence of `M05_methods_thermodynamics.tex`:
+
+> *"A key caveat: as we publish a generalized database that can be applied across
+> the kingdoms of life, transport reactions are scored from stoichiometry and
+> energy alone, and neither an estimate of membrane potential or pH gradients
+> are used."*
+
+One sentence, at the end of a paragraph about reporting conditions, with no
+follow-up anywhere in Results or Discussion. The reviewer is right that this is
+under-treated.
+
+**The compartment half of the comment is a documentation failure, not a data
+failure.** ModelSEED indices are *relative* — 0 is intracellular and 1 is
+extracellular relative to the reaction, resolved to named compartments
+(`c0`, `e0`, and organelles) when a template instantiates the reaction into a
+model. This is what lets one reaction serve a bacterial inner membrane, a
+mitochondrial membrane and a plastid envelope. `Biochemistry/REACTIONS.md` calls
+`m` a "compartment index number" and never explains this. It should, and the
+paper should say it in one clause.
+
+### What it costs — measured
+
+6,313 transport reactions (11.3% of the database).
+
+| | transport | non-transport |
+|---|---:|---:|
+| eQuilibrator commits to a direction | 24.4% | 17.4% |
+| dGPredictor commits to a direction | **1.2%** | 17.7% |
+| group contribution commits | 17.6% | 21.8% |
+| graded at all | 76.5% | 56.9% |
+| **gold / silver / bronze** | **27.7 / 14.6 / 57.7%** | 7.4 / 62.6 / 30.0% |
+
+dGPredictor effectively refuses transport altogether — 1.2% against 17.7% — so
+the transport grades rest on eQuilibrator and group contribution alone, and the
+cross-source corroboration the grading scheme depends on is thinner here than
+anywhere else in the database.
+
+Transport reactions are graded gold nearly four times as often as the rest of
+the database — which looks alarming until you see what the gold rests on. Of
+the 1,338 gold transport reactions, **95.3% carry ATP and ADP**, and only 2.5%
+translocate a proton at all. The deciding source is eQuilibrator for 1,231 of
+them.
+
+**That is the limitation, stated precisely:** for a primary active transporter
+the estimate is dominated by ATP hydrolysis, which every predictor estimates
+tightly, so the reaction earns a confident grade on the strength of its
+chemistry while the translocation — the part that needs a membrane potential
+and a pH gradient — contributes nothing to the score. The grade is trustworthy
+as a statement about the hydrolysis and should not be read as a statement about
+whether the transporter runs in that direction *in vivo*.
+
+Worth saying in our favour: the scheme does **not** over-claim on the easy
+cases. 4,056 transport reactions (64.2%) are uniport-like, with no net chemical
+change, and **none of them is gold** — 2,647 bronze, 975 ungraded, and 434
+silver. The 434 are worth understanding rather than glossing: with no net
+chemistry every source returns ΔG ≈ 0, the sources therefore scatter very
+little, and rule 3 (*corroborated*, $R \le 2$ and $z \le 2$) promotes them out
+of bronze. They agree because there is nothing to disagree about. That is a
+narrow, defensible artefact of the corroboration rule and it is better to name
+it than to let a reader find it. The failure mode that matters is confined to
+coupled transport.
+
+One more gap to disclose: `S04` excludes symmetrical transport from the LLM
+ensemble, so only 32.0% of transport reactions carry an LLM call against 81.5%
+of the database. Figure 3A does not show this, and its caption implies uniform
+coverage.
+
+### Proposed change
+
+1. **Expand the `M05` caveat** from one sentence to three, and move it out of
+   the conditions paragraph into its own `\paragraph{Transport.}`. Draft:
+
+   > \paragraph{Transport.} Compartments are recorded as relative indices —
+   > 0 intracellular, 1 extracellular — rather than named compartments, so that
+   > one reaction can be instantiated against a bacterial membrane, a
+   > mitochondrion or a plastid when a template builds a model. Because the
+   > database is not committed to a cell type, transport reactions are scored
+   > from stoichiometry and energy alone: no membrane potential and no
+   > transmembrane pH gradient enter the estimate. The consequence is specific.
+   > A primary active transporter carries ATP hydrolysis in its stoichiometry,
+   > every predictor estimates that tightly, and the reaction earns a confident
+   > grade from chemistry that is not the part in question — 96% of the
+   > gold-graded transport reactions are ATP-coupled, and only 2.5% translocate a
+   > proton. Those grades should be read as confidence in the coupled chemistry,
+   > not as evidence that the transporter runs in that direction in a given
+   > organism. Reactions whose only change is a compartment change carry no net
+   > chemistry, and none of them is graded gold: they are bronze or ungraded,
+   > apart from a small number the corroboration rule promotes to silver because
+   > every source agrees on an energy of approximately zero.
+
+2. **One sentence in `M14_discussion.tex`** naming this as future work — a
+   compartment-aware scoring layer needs organism context (Δψ, ΔpH,
+   stoichiometry of the coupling ion) that belongs to a model, not to a
+   reference database, and would be the natural next release.
+
+3. **Flag transport in the released grades.** Cheap and it forecloses the
+   objection: since `is_transport` already ships, either cap transport grades at
+   silver, or add an `evidence_caveat: transport` key to `thermo-evidence`.
+   Recommend the flag over the cap — capping destroys a real signal about the
+   hydrolysis, whereas a flag lets the user decide.
+
+4. **Fix `Biochemistry/REACTIONS.md`** to define the 0/1 convention as relative
+   indices, and note that Rhea's in/out maps onto it directly.
+
+**Author decision.** Item 3 changes shipped data, so it needs Chris/Sam. Items
+1, 2 and 4 are text-only.
+
+**Budget.** +5 sentences in Methods, +1 in Discussion. Offset: the magnesium
+detail in `M05` duplicates `S02` and can be cut to a pointer.
+
+---
+
+## R1 #3 — why only ~9,000 reactions are used for reconstructions
+
+> *"The authors mention that only 9,000 of the 56,000 reactions in the database
+> are used for reconstructions. It would be great if they could elaborate on
+> this. Is it because they are disconnected from the main core network? Is it
+> due to lack of GPR associations?"*
+
+**Origin.** First sentence of `M10_results_structure_curation.tex`:
+
+> *"We built and ran our curation pipeline on all structures that would impact
+> the set of roughly 9,000 reactions used in the ModelSEED and PlantSEED
+> reconstruction templates."*
+
+**The reviewer has misread this, and the sentence invites it.** That clause is
+scoping the *structure-curation effort* — we prioritised curation on the
+reactions the templates touch — not asserting that the other 47,000 reactions
+are unusable. Nothing else in the paper says how much of the database reaches a
+model. Given two reviewers read the paper and one drew this conclusion, the
+fix is to rewrite the sentence *and* add the explanation, not to correct the
+reviewer.
+
+### What is actually true
+
+The v7.0 templates (`ModelSEEDTemplates`, Gram-negative, Gram-positive,
+Archaea, Core) hold **8,597 unique base reactions** in union; 8,594 are live in
+the database. With PlantSEED_v3 roles that is the ~9,000 in the sentence.
+
+Both of the reviewer's hypotheses are worth answering directly, and neither is
+the main reason:
+
+- **Not GPR.** The Gram-negative template types its 8,584 reactions explicitly:
+  6,284 `conditional`, **2,258 `gapfilling`**, 31 `spontaneous`, 11 `universal`.
+  A quarter of the template is therefore present with no gene requirement at
+  all, as gapfilling candidates. (Complex references are sparser still — only
+  2,590 of the 6,284 `conditional` reactions carry one in the shipped JSON, the
+  rest presumably resolving their complexes through the role mapping at build
+  time; the `type` field is the authoritative classification and is what the
+  draft below cites.) A template is not a set of reactions with genes attached,
+  so missing GPRs cannot be what keeps reactions out.
+- **Not connectivity.** The templates are curated scopes, not the largest
+  connected component; nothing prunes on graph connectivity.
+
+The real reasons, in order:
+
+1. **A template is a curated prokaryotic scope, not a usability verdict.** The
+   database is a reference namespace spanning all kingdoms plus plants, plus
+   reactions from secondary databases and published models (8,107 reactions
+   carry only secondary-database or published-model aliases). Any one template
+   deliberately covers a fraction.
+2. **Mass and charge balance.** Only 28,096 of 48,403 live reactions (58%) are
+   balanced; an unbalanced reaction cannot enter a template. This is the same
+   structure-coverage limit the paper already reports, in a different guise.
+3. **Functional-role association.** Gene-driven reconstruction needs a mapping
+   to an annotated role. 20,019 live reactions (41.4%) carry an EC number.
+
+And the headroom is real and quantifiable: **14,041 live reactions are both
+balanced and EC-annotated, of which 8,089 are not in any v7.0 template.** That
+is roughly a doubling of template scope available without any new curation —
+a better answer than a defensive one.
+
+### Proposed change
+
+1. **Rewrite the `M10` opening sentence** so the scope claim cannot be read as
+   a coverage claim. Draft:
+
+   > We prioritised the curation pipeline on the compounds whose structures
+   > affect the ~9,000 reactions carried by the ModelSEED and PlantSEED
+   > reconstruction templates, since an error there propagates into every model
+   > built from them.
+
+2. **Add a short paragraph to `M13` or the Discussion** answering the question
+   the reviewer actually asked. Draft:
+
+   > A reconstruction template carries far fewer reactions than the database
+   > holds — the v7.0 ModelSEED templates span 8,597 — and the gap is not a
+   > statement that the remainder is unusable. The database is a reference
+   > namespace across all kingdoms and includes biochemistry from secondary
+   > databases and published models that no single template targets. What
+   > limits promotion into a template is mass and charge balance, which 58% of
+   > reactions currently achieve, and an association to an annotated functional
+   > role, which 41% carry; gene associations are not the constraint, since a
+   > quarter of the reactions already in a template are typed as gapfilling and
+   > carry no gene requirement at all. 14,041 reactions
+   > now satisfy both balance and role annotation, of which 8,089 sit outside
+   > every current template — the structure curation reported here is what makes
+   > that pool available.
+
+**Author decision.** Confirm the mechanism by which reactions enter a template
+(curated role mapping vs gapfill promotion) — that is a ModelSEEDTemplates
+process question I cannot settle from this repository, and the draft above is
+written to stay on the safe side of it.
+
+**Budget.** +1 paragraph. Offset: `M13` atom-mapping is two sentences and could
+absorb this without a new subsection.
+
+---
+
+## R1 #4 — the figures
+
+> *"The figures could use a little improvement. There is a bit of barplot abuse
+> (smiley face). For instance, Fig 1B would be more informative as a Venn
+> diagram. Fig 2A needs a color legend (and maybe a different color scheme)."*
+
+**Origin.** All three figures. The count is nine bar panels and one histogram
+panel across three figures — the complaint is fair on its face.
+
+| | current | verdict |
+|---|---|---|
+| 1A | grouped barh, 2020 vs 2026 per source | keep — magnitude over time, correct form |
+| 1B | stacked barh, unique vs shared | **replace** — see below |
+| 1C | stacked barh, complete vs incomplete | keep, or merge into 1A as a second encoding |
+| 2A | stacked barh, pKa provenance | **needs a legend** — see below |
+| 2B | plain barh, coverage per source | **weakest panel** — three numbers |
+| 2C | three histograms | keep — this one earns its space |
+| 3A | stacked barh, direction per source | keep — small-multiple comparison, correct form |
+| 3B/3C | stacked barh, grade composition | keep, but merge is possible |
+
+### 1B → Euler diagram
+
+The reviewer is right that the stacked bar throws away the interesting part:
+it says 67% of MetaCyc's reactions are unique but never says *who the other
+33% are shared with*. The regions exist and are well separated (live reactions,
+obsolete dropped):
+
+| region | reactions |
+|---|---:|
+| MetaCyc only | 18,896 |
+| Rhea only | 8,210 |
+| KEGG only | 5,294 |
+| all three | 3,465 |
+| MetaCyc ∩ Rhea | 2,267 |
+| MetaCyc ∩ KEGG | 1,515 |
+| KEGG ∩ Rhea | 569 |
+| in none of the three | 8,187 |
+
+Note these differ from the counts in `M09` (MetaCyc 31,805/21,258 etc.), which
+include obsolete reactions — see problem B above.
+
+A three-set Euler reads well at these proportions. `matplotlib-venn` is **not
+installed** in `~/Documents/py_venv`; either add it
+(`uv pip install matplotlib-venn`) or draw three circles as matplotlib patches,
+which is about fifteen lines and avoids a dependency for one panel. The 8,187
+reactions in none of the three should sit as a labelled box outside the
+circles, not be silently dropped.
+
+### 2A needs a legend
+
+The panel has four segments and labels two. Solid blue ("Marvin 29.5k") and
+hatched blue ("6.9k") are direct-labelled; the narrow dark-grey segment and the
+light-grey "8.2k" are not identified in the figure at all. The caption explains
+them in prose — *"Marvin reported no dissociable proton ... for 1,242 compounds.
+The remainder carry no structure to compute from"* — which is exactly the work
+a legend should do. Add a four-entry key: **Marvin (query)**, **Marvin (from
+SMILES)**, **no dissociable proton**, **no structure**.
+
+On the colour scheme: the panel currently spends its only hue on the two Marvin
+routes and renders both non-Marvin outcomes in grey, which visually merges a
+chemistry result ("no dissociable proton") with a curation gap ("no structure").
+Give the two grey segments distinct, non-sequential colours — they are not two
+degrees of the same thing.
+
+### Reduce the bar count
+
+Two changes remove two bar panels and add the panel comment 1 needs, leaving
+the figure count unchanged:
+
+- **Drop 2B**, or fold it into 2C as a coverage annotation on each histogram.
+  It carries three numbers (39%, 53%, 53%) and the text states all three.
+- **Replace 1B with the Euler**, and **add the pathway-class panel** from
+  comment 1 as 1D.
+
+### Also: Figure 3 has no panel D
+
+`M12_results_direction_sensitivity.tex` cites `Figure~\ref{fig:direction}D` for
+the grade counts. Figure 3 has panels A, B and C only; the caption defines no D.
+The reference should be to B and C, or the sentence should drop the pointer.
+This is a live cross-reference error and needs fixing regardless of the redesign.
+
+The same check across all three figures found this is the only broken pointer —
+but also that **panels 3B and 3C are never cited by letter anywhere in the
+text**, the only reference into Figure 3 being `A` and the non-existent `D`.
+Since 3B and 3C are the two panels that show what the grading scheme produces,
+retargeting the grading sentence at them fixes both problems at once.
+
+**Budget.** Neutral to negative — one panel removed, one added, one replaced.
+
+---
+
+## R2 #2 — what the LLM ensemble is actually for
+
+> *"The authors state in the Ensemble LLMs predictions section that a reaction may
+> be driven by a pathway in a cell, which may be different from their grading of
+> thermodynamic evidence. They then introduce LLMs and make predictions. Except
+> the numbers in Figure 3, it is not apparent how LLMs play roles in
+> reconstruction and other analyses."*
+
+**Origin.** `M06_methods_council_direction.tex`, `\subsection{Ensemble LLMs
+predictions}`, and `S04_direction_from_chemistry.tex`.
+
+**The reviewer has found a real gap between what the section promises and what
+it delivers.** The motivation opens on pathway-level driving force — *"every
+reaction is a member of a pathway within a cell, and the overarching drive of
+the pathway ... means a reaction may be driven in a direction on a level that
+supersedes our evaluation"* — cites three papers on exactly that, and then
+introduces an ensemble that sees only *"its name and stoichiometry alone"* and
+therefore has no pathway context at all. The section then withdraws twice: the
+calls *"do not [enter] our grading of the thermodynamic evidence"* (M06), and
+their confidence *"does not appear to distinguish correct calls ... and is not
+appropriate to use as a downstream filter"* (S04). A reader is left with a
+method that is motivated by something it does not do and disclaimed for
+everything else.
+
+**The role we can defend is coverage — but the accuracy has to be stated with
+it, and it is weaker than the headline agreement suggests.**
+
+Coverage, which is the good news:
+
+| | |
+|---|---:|
+| reactions carrying an LLM call | 45,644 (81.5%) |
+| reactions **no** thermodynamic source directs | 25,855 |
+| … of which the ensemble does direct | **22,902 (88.6%)** |
+
+Accuracy. `Biochemistry/Thermodynamics/SourceGrading/opentecr_comparison.csv`
+ships a measured `opentecr_dG_kJ` for 1,365 reactions, so the ensemble can be
+scored against **measurement** rather than against another prediction. Taking
+the measured direction from the sign of that energy:
+
+| source | margin | n (both commit) | agree | rate | chance | kappa |
+|---|---|---:|---:|---:|---:|---:|
+| LLM ensemble | sign only | 361 | 246 | 68.1% | 52.8% | 0.33 |
+| LLM ensemble | abs(dG) > 5 kJ | 264 | 200 | **75.8%** | 53.7% | 0.48 |
+| LLM ensemble | abs(dG) > 11.7 kJ | 158 | 115 | **72.8%** | 48.0% | 0.48 |
+| *eQuilibrator* | *sign only* | *127* | *126* | *99.2%* | *76.2%* | *0.97* |
+| *eQuilibrator* | *abs(dG) > 11.7 kJ* | *82* | *81* | *98.8%* | *66.4%* | *0.96* |
+
+*(Orientation check: the shipped eQuilibrator energy matches the sign of the
+measured energy on 183 of 185 anchored reactions, which is what establishes
+that `opentecr_dG_kJ` is already written in the ModelSEED equation's
+orientation. Applying the `ms_orientation_vs_canonical` column as a flip drops
+eQuilibrator to 45% and is the wrong reading — worth recording because it is an
+easy mistake to repeat.)*
+
+So the ensemble is right about three times in four where it commits and a
+measurement exists, against eQuilibrator's ~99%. The 94.7% agreement with
+eQuilibrator across the whole database (8,085 co-commitments, 7,659 agreeing)
+reads far better than that, but its **chance baseline is 88.3%** and Cohen's
+kappa is only **0.55**, because both sources overwhelmingly say "forward".
+
+The mechanism is worth reporting because it is so clean. **85.3% of the
+ensemble's calls are "forward as written"** (`>`), against 5.1% reverse and 7.8%
+reversible, and the errors sit entirely on one side. At the 11.7 kJ margin:
+
+| | LLM says `>` | LLM says `<` |
+|---|---:|---:|
+| **measured `>`** | 72 | 0 |
+| **measured `<`** | 43 | 43 |
+
+The ensemble is perfect when the answer is "forward" and a coin flip when it is
+not. It has learned the convention that reaction equations are written in the
+physiological direction — a genuine and useful prior — but it is not reading
+thermodynamics. **This is the most important thing to disclose**: it is
+recoverable from shipped data in an afternoon, so it is far better coming from
+us, and it independently justifies the decision the paper already made to keep
+these calls out of the evidence grading.
+
+### Proposed change
+
+1. **Rewrite the `M06` motivation** to stop promising pathway context. Draft
+   replacement for the subsection's first two sentences:
+
+   > Thermodynamic assignment is bounded by structural coverage: an energy
+   > cannot be computed for a reaction whose participants lack complete
+   > structures, however well understood the reaction is. That bound leaves
+   > 23,729 reactions with no direction from any predictor. We therefore run a
+   > complementary approach that does not depend on structures at all, using an
+   > ensemble — a "council" — of large language models to interpret a reaction
+   > from its name and stoichiometry.
+
+2. **State the role explicitly**, replacing the bare *"We integrate these
+   predictions transparently in our database, but do not include them in our
+   grading"*:
+
+   > The ensemble directs 22,902 of the reactions no thermodynamic source will
+   > commit on, roughly doubling the fraction of the database that carries a
+   > direction. Scored against the measured reactions where both commit, it
+   > recovers the correct direction 76% of the time, against 99% for
+   > eQuilibrator over the same anchors, and its errors fall almost entirely on
+   > reactions that run in reverse: 85% of its calls are the direction the
+   > equation is written in. We therefore release these calls as a separate,
+   > clearly-labelled layer and exclude them from the evidence grading. They are
+   > intended for reactions a reconstruction would otherwise have to leave
+   > reversible by default, and should be reviewed before use rather than
+   > trusted as evidence.
+
+3. **Add the forward-bias caveat to `S04`**, in the results subsection: the
+   85.3 / 5.1 / 7.8 call split, the 72/0 vs 43/43 confusion matrix, and the
+   point that agreement with eQuilibrator (94.7%) sits against an 88.3% chance
+   baseline, kappa 0.55. `S04` currently says only that the ensemble's
+   *confidence* does not discriminate; the sharper and more useful finding is
+   that its *direction* does not discriminate either, once the forward prior is
+   accounted for.
+
+4. **Fix the motivation's citations.** `mavrovouniotis1993`, `xu2008` and
+   `noor2014` support the pathway-driving-force argument being removed. If that
+   argument goes, either drop them or keep one sentence acknowledging that
+   pathway context can override a per-reaction thermodynamic call and that
+   neither approach here models it — which is true, and closes the loop with
+   R1's transport comment.
+
+**Author decision — the one real fork.** Either (a) keep the ensemble as a
+released dataset with a coverage role and a stated accuracy, as drafted above,
+or (b) give it a measured role in reconstruction by testing it on something.
+(b) needs work we have not done: take a draft model, compare the reactions left
+reversible by default against the ensemble's calls, and report how many
+directions change and whether the model's predictions improve. That is the
+analysis that would make the section unarguable, and it is the one the *title*
+used to promise before it was cut on 2026-09-15.
+
+The accuracy measurement above shifts the recommendation toward (a), and
+firmly. A 76%-accurate direction call whose errors concentrate on exactly the
+reactions a curator most needs help with is not something to build a
+reconstruction claim on; it is a coverage layer with a known bias, which is
+what the paper already treats it as. Recommend (a), with the bias disclosed.
+
+There is a third option worth naming, (c): **keep only the reverse and
+reversible calls as the useful signal.** The ensemble's `>` calls are nearly
+uninformative given the 85% prior, but a `<` call is the model going against
+that prior, and those are 2,350 reactions a curator could review in a
+tractable pass. If anyone wants the ensemble to do real work in this release,
+that framing costs nothing extra to compute and is defensible.
+
+**Budget.** Roughly neutral — the rewritten motivation is shorter than the one
+it replaces; the role sentences add about three lines.
+
+---
+
+## Summary of proposed actions
+
+| # | change | kind | blocked on |
+|---|---|---|---|
+| B | fix the 55% growth figure to 28% (one consistent population) | text | author sign-off |
+| 1a | re-run pathway/EC annotation over post-2020 reactions | **data/pipeline** | author sign-off |
+| 1b | new Figure 1 panel: MetaCyc class distribution, new vs old | figure | 1a |
+| 1c | 3 sentences in M09 | text | 1a |
+| 2a | expand M05 transport caveat to its own paragraph | text | — |
+| 2b | Discussion sentence on compartment-aware scoring | text | — |
+| 2c | flag transport reactions in released grades | **data** | Chris/Sam |
+| 2d | document the 0/1 convention in REACTIONS.md | docs | — |
+| 3a | rewrite M10 opening sentence | text | — |
+| 3b | paragraph on template scope vs database scope | text | template process Q |
+| 4a | Fig 1B → Euler (+`matplotlib-venn` or hand-drawn) | figure | — |
+| 4b | Fig 2A legend + recoloured non-Marvin segments | figure | — |
+| 4c | drop or fold Fig 2B | figure | — |
+| 4d | **fix `\ref{fig:direction}D` — panel D does not exist** | text | — |
+| 5a | rewrite M06 motivation, state coverage role + 76% accuracy | text | — |
+| 5b | forward-bias caveat + measured accuracy in S04 | text | — |
+| 5c | resolve the three orphaned citations | text | — |
+
+Net page budget: roughly neutral. The additions are concentrated in Methods
+(transport) and Results (pathways, templates); the offsets available are the
+magnesium detail in `M05`, the per-source reaction sentence in `M09`, and
+Figure 2B.