Phase 6 round-2 reviewer revisions: §III-H.1 promotion + framing alignment

Structural:
- Promote operational classifier definition from §III-L.0 to new §III-H.1, so
  the reader meets the five-way HC/MC/HSC/UN/LH rule before the §III-I/J/K
  diagnostic chain instead of ~130 lines after. §III-L renamed to
  "Anchor-Based Threshold Calibration"; §III-L.0 retains only calibration
  methodology, three units of analysis, any-pair semantics, and the FAR
  terminological note. §III-L.7 deleted (redundant with §III-J).
- Reorganise §V-H Limitations into Primary / Secondary / Documented features /
  Engineering groupings (was a flat 14-item list).
- Reframe §III-M from "ten-tool unsupervised-validation collection" to
  "each diagnostic addresses one specific unsupervised failure mode";
  rename "What v4.0 does/does not claim" → "Limits / Scope of the present
  analysis"; retitle Table XXVII.

Framing alignment (cross-section):
- Strip all v3.x / v4.0 / v3.20 / v4-new / inherited lineage labels from
  rendered text (Abstract, Intro, §II, §III, §IV, §V, §VI, Appendix, Impact).
- Replace "Paper A" rule references with "deployed" rule references.
- Soften "validation" to "characterise" / "check" / "screening label" /
  "consistency check" / "support"; "verdict" → "screening label".
- Remove codex-verified spike claims (non-Big-4 jittered dHash, Big-4 pooled
  cosine after firm-mean centring). Only formally scripted evidence
  (Scripts 39b–39e) retained; non-Big-4 evidence framed as corroborating
  raw-axis cosine, not as calibration evidence.
- Strip script-provenance parentheticals from Introduction; defer Script 39c
  internal references and similar to Methodology / Appendix.

Numerical / table fixes:
- §III-C document-count arithmetic: 12 corrupted → 13 corrupted/unreadable,
  verified against sqlite DB and total-pdf/ folder counts (90,282 - 4,198
  no-sig - 13 corrupted = 86,071 → 85,042 with detections → 182,328 sigs →
  168,755 CPA-matched). Table I shows VLM-positive (86,084) and
  processed-for-extraction (86,071) as separate rows.
- Wilson 95% CIs added for joint-rule ICCR rows in Table XXI / methodology
  table ([0.00011, 0.00018] and [0.00008, 0.00014]).
- Unit error fixed: 0.3856 pp / 0.4431 pp → 0.3856 (38.6 pp) / 0.4431 (44.3 pp).

Smaller revisions:
- Pipeline framing: "detecting" → "screening" in Abstract / Intro / Conclusion
  for consistency with the unsupervised-screening positioning.
- "hard ground-truth subset" → "conservative hard-positive subset" throughout.
- §III-F SSIM / pixel-comparison rebuttal compressed from ~15 lines to 4;
  design-level argument deferred to supplementary materials.
- "stakeholders can adopt / can derive thresholds" → "alternative operating
  points can be characterised by inverting" (less prescriptive).
- "the same mechanism extending in milder form to Firms B/C/D" → "similar,
  milder production-related reuse patterns at Firms B/C/D" (mechanism claim
  softened).
- Appendix A "non-hand-signed mode" / "two-mechanism mixture" lineage language
  aligned with v4 framing.

Appendix B:
- Rebuilt as a redirect-only stub. The HTML-commented obsolete table mapping
  (Table IX–XVIII labels with FAR / capture-rate / validation language) is
  removed; replaced with a short paragraph pointing to supplementary
  materials for full table-to-script provenance.

Cross-references:
- All §III-L references for the rule definition retargeted to §III-H.1;
  references for calibration still point to §III-L.
- §III-H references for byte-level Firm A evidence / non-Big-4 reverse anchor
  retargeted to §III-H.2.

Artefacts:
- Combined manuscript regenerated: paper_a_v4_combined.md, 1314 lines
  (was 1346 pre-review).
- Two review handoff documents added:
  paper/review_handoff_abstract_intro_20260515.md
  paper/review_handoff_body_20260515.md

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

paper/paper_a_related_work_v3.md

@@ -15,8 +15,8 @@ Hafemann et al. [16] further addressed the practical challenge of adapting to ne
 A common thread in this literature is the assumption that the primary threat is *identity fraud*: a forger attempting to produce a convincing imitation of another person's signature.
 Our work addresses a fundamentally different problem---detecting whether the *legitimate signer's* stored signature image has been reproduced across many documents---which requires analyzing the upper tail of the intra-signer similarity distribution rather than modeling inter-signer discriminability.
 
-Brimoh and Olisah [8] proposed a consensus-threshold approach that derives classification boundaries from known genuine reference pairs, the methodology most closely related to our calibration strategy.
-However, their method operates on standard verification benchmarks with laboratory-collected signatures, whereas our approach applies threshold calibration using a replication-dominated subpopulation identified through domain expertise in real-world regulatory documents.
+Brimoh and Olisah [8] are closest in spirit in using reference evidence to discipline threshold choice.
+Their setting, however, uses standard verification benchmarks with known genuine references, whereas our archival setting lacks signature-level labels and therefore characterises a fixed deployed screening rule through inter-CPA coincidence-rate anchors.
 
 ## B. Document Forensics and Copy Detection
 
@@ -51,9 +51,9 @@ Chamakh and Bounouh [22] confirmed that a simple ResNet backbone with cosine sim
 Babenko et al. [23] established that CNN-extracted neural codes with cosine similarity provide an effective framework for image retrieval and matching, a finding that underpins our feature-comparison approach.
 These findings collectively suggest that pre-trained CNN features, when L2-normalized and compared via cosine similarity, provide a robust and computationally efficient representation for signature comparison---particularly suitable for large-scale applications where the computational overhead of Siamese training or metric learning is impractical.
 
-## E. Statistical Methods for Threshold Determination
+## E. Statistical Methods for Threshold Characterisation and Calibration
 
-Our threshold-determination framework combines three families of methods developed in statistics and accounting-econometrics.
+Our threshold-characterisation and calibration framework combines three families of methods developed in statistics and accounting-econometrics.
 
 *Non-parametric density estimation.*
 Kernel density estimation [28] provides a smooth estimate of a similarity distribution without parametric assumptions.
@@ -71,10 +71,10 @@ When the empirical distribution is viewed as a weighted sum of two (or more) lat
 For observations bounded on $[0,1]$---such as cosine similarity and normalized Hamming-based dHash similarity---the Beta distribution is the natural parametric choice, with applications spanning bioinformatics and Bayesian estimation.
 Under mild regularity conditions, White's quasi-MLE result [41] supports interpreting maximum-likelihood estimates under a mis-specified parametric family as consistent estimators of the pseudo-true parameter that minimizes the Kullback-Leibler divergence to the data-generating distribution within that family; we use this result to justify the Beta-mixture fit as a principled approximation rather than as a guarantee that the true distribution is Beta.
 
-The present study combines all three families, using each to produce an independent threshold estimate and treating cross-method convergence---or principled divergence---as evidence of where in the analysis hierarchy the mixture structure is statistically supported.
+The present study uses these tools diagnostically: first to test whether the descriptor distribution supports a natural operating boundary, and then, when that support fails under composition decomposition, to motivate anchor-based ICCR calibration of a fixed deployed rule.
 
 *Cross-validation in a small-cluster scope.*
-Cross-validation methodology in the leave-one-out tradition has been developed extensively in statistics since Stone [42] and Geisser [43], and modern surveys including Vehtari et al. [44] discuss its application to mixture models. In document-forensics calibration the technique has been used selectively, typically with the individual document or signature as the hold-out unit. Our application in §III-K differs in two respects from the standard usage: (i) the hold-out unit is the *firm* (not the individual CPA or signature), so the analysis directly probes cross-firm reproducibility of the fitted mixture rather than within-firm sampling variance; and (ii) the held-out predictions are interpreted as a *composition-sensitivity band* on the candidate mixture boundary, not as a sufficiency claim for the inherited five-way operational classifier (which is calibrated separately; §III-L). We treat LOOO drift as descriptive information about how the mixture characterisation moves when training composition changes, not as a pass/fail test for the operational classifier.
+Cross-validation methodology in the leave-one-out tradition has been developed extensively in statistics since Stone [42] and Geisser [43], and modern surveys including Vehtari et al. [44] discuss its application to mixture models. In document-forensics calibration the technique has been used selectively, typically with the individual document or signature as the hold-out unit. Our application in §III-K differs in two respects from the standard usage: (i) the hold-out unit is the *firm* (not the individual CPA or signature), so the analysis directly probes cross-firm reproducibility of the fitted mixture rather than within-firm sampling variance; and (ii) the held-out predictions are interpreted as a *composition-sensitivity band* on the candidate mixture boundary, not as a sufficiency claim for the deployed five-way operational classifier (§III-H.1; calibrated separately in §III-L). We treat LOOO drift as descriptive information about how the mixture characterisation moves when training composition changes, not as a pass/fail test for the operational classifier.
 <!--
 REFERENCES for Related Work (see paper_a_references_v3.md for full list):
 [3]  Bromley et al. 1993 — Siamese TDNN (NeurIPS)