Paper A v3.18.2: address codex GPT-5.5 round-16 Minor-Revision findings

Codex independent peer review (paper/codex_review_gpt55_v3_18_1.md) audited
empirical claims against scripts/JSON reports rather than rubber-stamping
prior Accept verdicts. Verdict: Minor Revision. This commit addresses every
flagged item.

- Soften mechanism-identification language (Results IV-D.1, Discussion B):
  per-signature cosine "fails to reject unimodality" rather than "reflects a
  single dominant generative mechanism"; framing tied to joint evidence.
- Replace overabsolute "single stored image" with multi-template phrasing
  in Introduction and Methodology III-A.
- Reframe Methodology III-H so practitioner knowledge is non-load-bearing;
  evidentiary basis is the paper's own image evidence.
- Fix stale section cross-references after the v3.18 retitling: IV-F.* ->
  IV-G.* in 11 locations across methodology and results.
- Fix 0.941 / 0.945 / 0.9407 wording in Methodology III-K to use the
  calibration-fold P5 = 0.9407 and the rounded sensitivity cut 0.945.
- Soften "sharp discontinuity" in Results IV-G.3 to "23-28 percentage-point
  gap consistent with firm-wide non-hand-signing practice".
- Soften Conclusion's "directly generalizable" with explicit conditions on
  analogous anchors and artifact-generation physics.
- Add Appendix B: table-to-script provenance map (15 manuscript tables
  mapped to generating scripts and JSON report artifacts).
- New script signature_analysis/28_byte_identity_decomposition.py produces
  reproducible artifacts for two previously-unverified claims:
  (a) 145 / 50 / 180 / 35 Firm A byte-identity decomposition (verified);
  (b) cross-firm dual-descriptor convergence -- corrected from the previous
      manuscript text "non-Firm-A 11.3% vs Firm A 58.7% (5x)" to the
      database-verified "non-Firm-A 42.12% vs Firm A 88.32% (~2.1x)".
- Clarify scripts 19 / 21 docstrings: legacy EER / FRR / Precision / F1
  helpers are retained for diagnostic use only and are NOT cited as
  biometric performance in the paper. Remove "interview evidence" wording.
- Rebuild Paper_A_IEEE_Access_Draft_v3.docx.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

paper/paper_a_results_v3.md

@@ -73,9 +73,9 @@ The $N = 168{,}740$ count used in Table V and in the downstream same-CPA per-sig
 | All-CPA min dHash (independent) | 168,740 | 0.0468 | <0.001 | Multimodal |
 -->
 
-Firm A's per-signature cosine distribution is *unimodal* ($p = 0.17$), reflecting a single dominant generative mechanism (non-hand-signing) with a long left tail attributable to within-firm heterogeneity in signing outputs (Section III-G discusses the scope of partner-level claims).
+Firm A's per-signature cosine distribution *fails to reject unimodality* ($p = 0.17$), a pattern consistent with a dominant high-similarity regime plus a long left tail attributable to within-firm heterogeneity in signing outputs (Section III-G discusses the scope of partner-level claims).
 The all-CPA cosine distribution, which mixes many firms with heterogeneous signing practices, is *multimodal* ($p < 0.001$).
-The Firm A unimodal-long-tail finding is the structural evidence that supports the replication-dominated framing (Section III-H): a single dominant mechanism plus residual within-firm heterogeneity, rather than two cleanly separated mechanisms.
+The Firm A unimodal-long-tail finding is, in conjunction with the byte-identity, partner-ranking, and intra-report evidence reported below, consistent with the replication-dominated framing (Section III-H): a dominant high-similarity regime plus residual within-firm heterogeneity, rather than two cleanly separated mechanisms.
 
 ### 2) Burgstahler-Dichev / McCrary Density-Smoothness Diagnostic
 
@@ -204,7 +204,7 @@ Under this proper test the two extreme rules agree across folds (cosine $> 0.837
 The operationally relevant rules in the 85–95% capture band differ between folds by 1–5 percentage points ($p < 0.001$ given the $n \approx 45\text{k}/15\text{k}$ fold sizes).
 Both folds nevertheless sit in the same replication-dominated regime: every calibration-fold rate in the 85–99% range has a held-out counterpart in the 87–99% range, and the operational dual rule cosine $> 0.95$ AND $\text{dHash}_\text{indep} \leq 8$ captures 89.40% of the calibration fold and 91.54% of the held-out fold.
 The modest fold gap is consistent with within-Firm-A heterogeneity in replication intensity: the random 30% CPA sample evidently contained proportionally more high-replication CPAs.
-We therefore interpret the held-out fold as confirming the qualitative finding (Firm A is strongly replication-dominated across both folds) while cautioning that exact rates carry fold-level sampling noise that a single 30% split cannot eliminate; the threshold-independent partner-ranking analysis (Section IV-F.2) is the cross-check that is robust to this fold variance.
+We therefore interpret the held-out fold as confirming the qualitative finding (Firm A is strongly replication-dominated across both folds) while cautioning that exact rates carry fold-level sampling noise that a single 30% split cannot eliminate; the threshold-independent partner-ranking analysis (Section IV-G.2) is the cross-check that is robust to this fold variance.
 
 ### 3) Operational-Threshold Sensitivity: cos $> 0.95$ vs cos $> 0.945$
 
@@ -332,7 +332,7 @@ A report is "in agreement" if both signature labels fall in the same coarse buck
 
 Firm A achieves 89.9% intra-report agreement, with 87.5% of Firm A reports having *both* signers classified as non-hand-signed and only 4 reports (0.01%) having both classified as likely hand-signed.
 The other Big-4 firms (B, C, D) and non-Big-4 firms cluster at 62-67% agreement, a 23-28 percentage-point gap.
-This sharp discontinuity in intra-report agreement between Firm A and the other firms is the pattern predicted by firm-wide (rather than partner-specific) non-hand-signing practice.
+This 23-28 percentage-point gap in intra-report agreement between Firm A and the other firms is consistent with firm-wide (rather than partner-specific) non-hand-signing practice; we do not claim a sharp discontinuity in the formal sense, since classifier calibration, firm-specific document-production pipelines, and signer-mix differences could each contribute to gap magnitude.
 
 We note that this test uses the calibrated classifier of Section III-K rather than a threshold-free statistic; the substantive evidence lies in the *cross-firm gap* between Firm A and the other firms rather than in the absolute agreement rate at any single firm, and that gap is robust to moderate shifts in the absolute cutoff so long as the cutoff is applied uniformly across firms.
 
@@ -341,7 +341,7 @@ We note that this test uses the calibrated classifier of Section III-K rather th
 Table XVII presents the final classification results under the dual-descriptor framework with Firm A-calibrated thresholds for 84,386 documents.
 The document count (84,386) differs from the 85,042 documents with any YOLO detection (Table III) because 656 documents carry only a single detected signature, for which no same-CPA pairwise comparison and therefore no best-match cosine / min dHash statistic is available; those documents are excluded from the classification reported here.
 We emphasize that the document-level proportions below reflect the *worst-case aggregation rule* of Section III-K: a report carrying one stamped signature and one hand-signed signature is labeled with the most-replication-consistent of the two signature-level verdicts.
-Document-level rates therefore represent the share of reports in which *at least one* signature is non-hand-signed rather than the share in which *both* are; the intra-report agreement analysis of Section IV-F.3 (Table XVI) reports how frequently the two co-signers share the same signature-level label within each firm, so that readers can judge what fraction of the non-hand-signed document-level share corresponds to fully non-hand-signed reports versus mixed reports.
+Document-level rates therefore represent the share of reports in which *at least one* signature is non-hand-signed rather than the share in which *both* are; the intra-report agreement analysis of Section IV-G.3 (Table XVI) reports how frequently the two co-signers share the same signature-level label within each firm, so that readers can judge what fraction of the non-hand-signed document-level share corresponds to fully non-hand-signed reports versus mixed reports.
 
 <!-- TABLE XVII: Document-Level Classification (Dual-Descriptor: Cosine + dHash)
 | Verdict | N (PDFs) | % | Firm A | Firm A % |
@@ -370,8 +370,9 @@ We note that because the non-hand-signed thresholds are themselves calibrated to
 
 ### 2) Cross-Firm Comparison of Dual-Descriptor Convergence
 
-Among non-Firm-A CPAs with cosine $> 0.95$, only 11.3% exhibit dHash $\leq 5$, compared to 58.7% for Firm A---a five-fold difference that demonstrates the discriminative power of the structural verification layer.
-This cross-firm gap is consistent with firm-wide non-hand-signing practice at Firm A versus partner-specific or per-engagement replication at other firms; it complements the partner-level ranking (Section IV-F.2) and intra-report consistency (Section IV-F.3) findings.
+Among the 65,515 non-Firm-A signatures with per-signature best-match cosine $> 0.95$, 42.12% have $\text{dHash}_\text{indep} \leq 5$, compared to 88.32% of the 55,921 Firm A signatures meeting the same cosine condition---a $\sim 2.1\times$ difference that the structural-verification layer makes visible.
+This cross-firm gap is consistent with firm-wide non-hand-signing practice at Firm A versus partner-specific or per-engagement replication at other firms; it complements the partner-level ranking (Section IV-G.2) and intra-report consistency (Section IV-G.3) findings.
+Counts and percentages are reproduced by `signature_analysis/28_byte_identity_decomposition.py` and reported in `reports/byte_identity_decomp/byte_identity_decomposition.json` (see Appendix B for the table-to-script provenance map).
 
 ## I. Ablation Study: Feature Backbone Comparison