Paper A v13 rev8: fusion-review revision (29 items) + verified data analysis

Address all 29 items from the fused reviewer report (Gemini 3.1 Pro +
ChatGPT 5.5 + Opus 4.8): 3 fatal, 4 severe, arbitration A/B, 5 fusion-new,
15 minor. All new numbers computed from signature_analysis.db; nothing
fabricated.

Claim honesty (F1/F3/F4/F7/G3):
- Retract all "139x the floor" comparisons; ICCR -> between-accountant
  specificity proxy throughout; state within-accountant FPR is not
  estimable and ICCR is not even a bound (anti-conservative direction).
- Firm A reframed as quasi-positive known-positive benchmark (not blinded).
- byte-identity recast as prevalence signal, not a recall/sanity check.
- tunable -> single-direction conservativeness dial (no P-R frontier).

New data analysis (verified, bit-reproducible via committed scripts):
- F2/G1 (Sec V-B): 880-PDF imaging-pipeline audit (Table V) - plain scans
  82% (2013) -> 1% (2021); producer strings name scanner hardware
  (Fuji Xerox D125 etc.); substrate transforms at 2020/21 = named confound.
- F5 (Sec IV-C): four robustness checks - pool-size stratification,
  accountant-clustered bootstrap (gap 53.7pp [49.5,57.5]), firm+year FE
  logistic (B/C/D OR 0.06-0.12), leave-one-year-out (gap 53.1-54.9pp).
- byte-identity era split: 30 scan-era (18 Firm A, pipeline-robust) vs
  232 digital-era (detectability-inflated, hedged).
- G5: archive-wide 888 expected chance HC flags [677,1098].
- M4: Figure 3 replaced with real 2D density (n=150,441).

Structure/minor: abstract restructured (M1); operational definition (M2);
interview disclaimer (M3); Threats to Validity subsection (M8); review
protocol framed as design not evidence (M9); N reconciliations (M10/M11);
Table II-c 2020-23 five-way (M12); Section refs, American spelling,
notation table (M5/M13/M15); reference URLs verified (M14).

Open (author-only): placeholders (M13), II-b/IV table merge (M15).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Qn59FdF9JMyfFg3sjcUNNG

paper/v13_build/scripts/make_fig3_density.py

@@ -0,0 +1,84 @@
+"""Figure 3 (real data version): 2D density of the two measures over the five-region scheme.
+Replaces the earlier schematic with the actual distribution, with axis ticks and the rule cuts.
+Reproduces from signature_analysis.db; Big-4, is_valid=1, both measures present."""
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+from matplotlib.patches import Rectangle
+import numpy as np
+import sqlite3
+
+DB = "/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db"
+BIG4 = ('勤業眾信聯合', '資誠聯合', '安侯建業聯合', '安永聯合')
+
+con = sqlite3.connect(DB)
+cur = con.cursor()
+cur.execute(f"""
+SELECT max_similarity_to_same_accountant, min_dhash_independent
+FROM signatures
+WHERE is_valid=1 AND max_similarity_to_same_accountant IS NOT NULL
+  AND min_dhash_independent IS NOT NULL
+  AND excel_firm IN ({','.join(['?']*4)})
+""", BIG4)
+rows = cur.fetchall()
+con.close()
+cos = np.array([r[0] for r in rows], dtype=float)
+dh = np.array([r[1] for r in rows], dtype=float)
+n = len(cos)
+
+LO, HI = 0.8547, 0.95
+DH1, DH2 = 5, 15
+xmin, xmax = 0.70, 1.002
+ymin, ymax = -0.5, 30
+ycap = 30  # display cap; values above are piled into the top row for visibility
+
+dh_disp = np.minimum(dh, ycap - 0.5)
+
+fig, ax = plt.subplots(figsize=(5.6, 4.4))
+
+# faint region tint behind the density
+ax.add_patch(Rectangle((xmin, ymin), LO - xmin, ymax - ymin, facecolor='#bdc3c7', alpha=0.12, zorder=0))
+ax.add_patch(Rectangle((LO, ymin), HI - LO, ymax - ymin, facecolor='#f7dc6f', alpha=0.12, zorder=0))
+ax.add_patch(Rectangle((HI, ymin), xmax - HI, DH1 - ymin, facecolor='#cb4335', alpha=0.14, zorder=0))
+ax.add_patch(Rectangle((HI, DH1), xmax - HI, DH2 - DH1, facecolor='#eb984e', alpha=0.14, zorder=0))
+ax.add_patch(Rectangle((HI, DH2), xmax - HI, ymax - DH2, facecolor='#aed6f1', alpha=0.14, zorder=0))
+
+# real 2D density (log counts)
+xedges = np.linspace(xmin, xmax, 90)
+yedges = np.arange(-0.5, ycap + 0.5, 1.0)  # integer dHash bins
+H, xe, ye = np.histogram2d(cos, dh_disp, bins=[xedges, yedges])
+pcm = ax.pcolormesh(xe, ye, H.T, norm=LogNorm(vmin=1, vmax=H.max()),
+                    cmap='viridis', zorder=1, shading='flat')
+cb = fig.colorbar(pcm, ax=ax, pad=0.02)
+cb.set_label('signatures per cell (log scale)', fontsize=8)
+cb.ax.tick_params(labelsize=7)
+
+# cut lines
+ax.axvline(LO, color='gray', ls=':', lw=1.1, zorder=3)
+ax.axvline(HI, color='black', ls='--', lw=1.1, zorder=3)
+ax.plot([HI, xmax], [DH1, DH1], 'k--', lw=0.9, zorder=3)
+ax.plot([HI, xmax], [DH2, DH2], 'k--', lw=0.9, zorder=3)
+
+# region labels
+ax.text((xmin + LO) / 2, 24, 'LH', ha='center', fontsize=10, weight='bold', color='#34495e', zorder=4)
+ax.text((LO + HI) / 2, 24, 'UN', ha='center', fontsize=10, weight='bold', color='#7d6608', zorder=4)
+ax.text((HI + xmax) / 2, 2.2, 'HC', ha='center', fontsize=10, weight='bold', color='#cb4335', zorder=4)
+ax.text((HI + xmax) / 2, 9.7, 'MC', ha='center', fontsize=10, weight='bold', color='#a04000', zorder=4)
+ax.text((HI + xmax) / 2, 24, 'HSC', ha='center', fontsize=9, weight='bold', color='#21618c', zorder=4)
+
+ax.set_xlim(xmin, xmax)
+ax.set_ylim(ymin, ymax)
+ax.set_xticks([0.70, 0.75, 0.80, 0.8547, 0.90, 0.95, 1.00])
+ax.set_xticklabels(['0.70', '0.75', '0.80', '0.855', '0.90', '0.95', '1.00'], fontsize=7.5)
+ax.set_yticks([0, 5, 10, 15, 20, 25, 30])
+ax.set_yticklabels(['0', '5', '10', '15', '20', '25', '≥30'], fontsize=7.5)
+ax.set_xlabel('cosine similarity to same accountant (style)', fontsize=9)
+ax.set_ylabel('min dHash distance (structure)', fontsize=9)
+ax.set_title(f'Figure 3. Two-measure plane: real density over the five regions (Big-4, n={n:,})',
+             fontsize=8.5)
+fig.tight_layout()
+out = '/Volumes/NV2/pdf_recognize/paper/v13_build/figures/fig3.png'
+fig.savefig(out, dpi=200, bbox_inches='tight')
+plt.close(fig)
+print(f'fig3 density OK: n={n:,}, dHash>=30 piled: {(dh>=ycap).sum()}, written {out}')