Paper A v13: filled submission draft (rev7) + reproducible build bundle

Fill all 18 placeholders in the condensed v13 submission draft with
data verified against the analysis DB and LOCKED canonical scripts;
close 12/13 co-author review items (only #8b protocol first-run open).

Key changes (need co-author sign-off; see handoff doc):
- Firm A out-of-sample HC 0.01% -> 0.42% (buggy 0.0001 from Script 49
  same-pair bug, propagated v4.2->v13; never reuse 0.0001)
- §III-D empty cell ~=0 -> 7,681 honest reframe (not degenerate crops)
- low cosine cut 0.837 -> 0.8547 primary (BCD 2013-2019 closed-world,
  held-out discipline; 0.8489 confirmed = BCD all-period); HC/MC/HSC
  unchanged, UN/LH move <=0.4pp

Adds Figures 1-5 (real-data plots + schematics), full references,
Appendix A/B, UN/HSC ICCR, n-reconciliation, #13 MOPS-metadata
survival verification, "參" set-level feasibility probe (negative).
Two codex (gpt-5.5) adversarial rounds applied; no fabrication found.

Bundle: paper/v13_build/ (markdown source, harvest/figure scripts,
figures) for reproducibility. Handoff note for co-author included.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

paper/v13_build/scripts/spike_setlevel.py

@@ -0,0 +1,49 @@
+import sqlite3, numpy as np
+DB='/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db'
+BCD=('安侯建業聯合','資誠聯合','安永聯合')
+c=sqlite3.connect(f'file:{DB}?mode=ro',uri=True)
+rows=c.execute("""SELECT s.assigned_accountant, s.max_similarity_to_same_accountant, s.min_dhash_independent
+  FROM signatures s JOIN accountants a ON s.assigned_accountant=a.name
+  WHERE a.firm IN ('安侯建業聯合','資誠聯合','安永聯合')
+    AND CAST(substr(s.year_month,1,4) AS INT) BETWEEN 2013 AND 2019
+    AND s.max_similarity_to_same_accountant IS NOT NULL AND s.min_dhash_independent IS NOT NULL""").fetchall()
+from collections import defaultdict
+by=defaultdict(list)
+for a,cos,dh in rows: by[a].append((cos,dh))
+accs={a:np.array(v) for a,v in by.items() if len(v)>=15}
+print(f"BCD 2013-2019: {len(accs)} accountants with >=15 signatures (of {len(by)} total)")
+
+rep=[]; tight=[]; rem_med=[]; klass=[]
+for a,v in accs.items():
+    cos=v[:,0]; dh=v[:,1]
+    hc=(cos>0.95)&(dh<=5)
+    rf=hc.mean(); tf=(cos>0.95).mean()
+    isolated=cos[cos<=0.95]
+    rm=np.median(isolated) if len(isolated)>=3 else np.nan
+    rep.append(rf); tight.append(tf); rem_med.append(rm)
+    klass.append('pure-hand' if rf<0.10 else ('pure-stamp' if rf>0.90 else 'mixed'))
+rep=np.array(rep); tight=np.array(tight); rem_med=np.array(rem_med); klass=np.array(klass)
+
+import collections
+print("\n=== Per-accountant replication-fraction (HC share) distribution ===")
+for lo,hi in [(0,0.1),(0.1,0.3),(0.3,0.5),(0.5,0.7),(0.7,0.9),(0.9,1.01)]:
+    n=((rep>=lo)&(rep<hi)).sum(); print(f"  rep_frac [{lo:.1f},{hi:.1f}): {n:3d} accountants")
+print("  class counts:", dict(collections.Counter(klass)))
+
+mixed=klass=='mixed'
+print(f"\n=== MIXED accountants (n={mixed.sum()}): is the non-tight remainder dispersed (separable)? ===")
+rm_mixed=rem_med[mixed & ~np.isnan(rem_med)]
+print(f"  remainder (cos<=0.95) median cosine across mixed accountants: median={np.median(rm_mixed):.3f}, IQR[{np.percentile(rm_mixed,25):.3f},{np.percentile(rm_mixed,75):.3f}]")
+print(f"  fraction of mixed accountants whose remainder median < 0.90 (clearly dispersed): {(rm_mixed<0.90).mean():.2f}")
+print(f"  fraction with remainder median < 0.85 (very dispersed): {(rm_mixed<0.85).mean():.2f}")
+# gap between tight group (cos>0.95) and remainder: per mixed accountant
+gaps=[]
+for a,v in accs.items():
+    cos=v[:,0]
+    t=cos[cos>0.95]; r=cos[cos<=0.95]
+    if len(t)>=3 and len(r)>=3:
+        gaps.append(np.median(t)-np.median(r))
+gaps=np.array(gaps)
+print(f"\n=== Tight-vs-remainder cosine gap (all accountants with both parts, n={len(gaps)}) ===")
+print(f"  median gap = {np.median(gaps):.3f}  (large gap => two-component structure is real & separable)")
+print(f"  fraction with gap > 0.10: {(gaps>0.10).mean():.2f}")