Paper A v3.5: resolve codex round-4 residual issues

Fully addresses the partial-resolution / unfixed items from codex
gpt-5.4 round-4 review (codex_review_gpt54_v3_4.md):

Critical
- Table XI z/p columns now reproduce from displayed counts. Earlier
  table had 1-4-unit transcription errors in k values and a fabricated
  cos > 0.9407 calibration row; both fixed by rerunning Script 24
  with cos = 0.9407 added to COS_RULES and copying exact values from
  the JSON output.
- Section III-L classifier now defined entirely in terms of the
  independent-minimum dHash statistic that the deployed code (Scripts
  21, 23, 24) actually uses; the legacy "cosine-conditional dHash"
  language is removed. Tables IX, XI, XII, XVI are now arithmetically
  consistent with the III-L classifier definition.
- "0.95 not calibrated to Firm A" inconsistency reconciled: Section
  III-H now correctly says 0.95 is the whole-sample Firm A P95 of the
  per-signature cosine distribution, matching III-L and IV-F.

Major
- Abstract trimmed to 246 words (from 367) to meet IEEE Access 250-word
  limit. Removed "we break the circularity" overclaim; replaced with
  "report capture rates on both folds with Wilson 95% intervals to
  make fold-level variance visible".
- Conclusion mirrors the Abstract reframe: 70/30 split documents
  within-firm sampling variance, not external generalization.
- Introduction no longer promises precision / F1 / EER metrics that
  Methods/Results don't deliver; replaced with anchor-based capture /
  FAR + Wilson CI language.
- Section III-G within-auditor-year empirical-check wording corrected:
  intra-report consistency (IV-H.3) is a different test (two co-signers
  on the same report, firm-level homogeneity) and is not a within-CPA
  year-level mixing check; the assumption is maintained as a bounded
  identification convention.
- Section III-H "two analyses fully threshold-free" corrected to "only
  the partner-level ranking is threshold-free"; longitudinal-stability
  uses 0.95 cutoff, intra-report uses the operational classifier.

Minor
- Impact Statement removed from export_v3.py SECTIONS list (IEEE Access
  Regular Papers do not have a standalone Impact Statement). The file
  itself is retained as an archived non-paper note for cover-letter /
  grant-report reuse, with a clear archive header.
- All 7 previously unused references ([27] dHash, [31][32] partner-
  signature mandates, [33] Taiwan partner rotation, [34] YOLO original,
  [35] VLM survey, [36] Mann-Whitney) are now cited in-text:
    [27] in Methodology III-E (dHash definition)
    [31][32][33] in Introduction (audit-quality regulation context)
    [34][35] in Methodology III-C/III-D
    [36] in Results IV-C (Mann-Whitney result)

Updated Script 24 to include cos = 0.9407 in COS_RULES so Table XI's
calibration-fold P5 row is computed from the same data file as the
other rows.

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

paper/paper_a_introduction_v3.md

@@ -12,6 +12,7 @@ This reproduction can occur either through an administrative stamping workflow--
 From the perspective of the output image the two workflows are equivalent: both yield a pixel-level reproduction of a single stored image on every report the partner signs off, so that signatures on different reports of the same partner are identical up to reproduction noise.
 We refer to signatures produced by either workflow collectively as *non-hand-signed*.
 Although this practice may fall within the literal statutory requirement of "signature or seal," it raises substantive concerns about audit quality, as an identically reproduced signature applied across hundreds of reports may not represent meaningful individual attestation for each engagement.
+The accounting literature has long examined the audit-quality consequences of partner-level engagement transparency: studies of partner-signature mandates in the United Kingdom find measurable downstream effects [31], cross-jurisdictional evidence on individual partner signature requirements highlights similar quality channels [32], and Taiwan-specific evidence on mandatory partner rotation documents how individual-partner identification interacts with audit-quality outcomes [33].
 Unlike traditional signature forgery, where a third party attempts to imitate another person's handwriting, non-hand-signing involves the legitimate signer's own stored signature being reused.
 This practice, while potentially widespread, is visually invisible to report users and virtually undetectable through manual inspection at scale: regulatory agencies overseeing thousands of publicly listed companies cannot feasibly examine each signature for evidence of image reproduction.
 
@@ -25,7 +26,7 @@ This detection problem differs fundamentally from forgery detection: while it do
 A secondary methodological concern shapes the research design.
 Many prior similarity-based classification studies rely on ad-hoc thresholds---declaring two images equivalent above a hand-picked cosine cutoff, for example---without principled statistical justification.
 Such thresholds are fragile and invite reviewer skepticism, particularly in an archival-data setting where the cost of misclassification propagates into downstream inference.
-A defensible approach requires (i) a statistically principled threshold-determination procedure, ideally anchored to an empirical reference population drawn from the target corpus; (ii) convergent validation across multiple threshold-determination methods that rest on different distributional assumptions; and (iii) external validation against anchor populations with known ground-truth characteristics using precision, recall, $F_1$, and equal-error-rate metrics that prevail in the biometric-verification literature.
+A defensible approach requires (i) a statistically principled threshold-determination procedure, ideally anchored to an empirical reference population drawn from the target corpus; (ii) convergent validation across multiple threshold-determination methods that rest on different distributional assumptions; and (iii) external validation against naturally-occurring anchor populations---byte-level identical pairs as a conservative gold positive subset and large random inter-CPA pairs as a gold negative population---reported with Wilson 95% confidence intervals on per-rule capture / FAR rates, since precision and $F_1$ are not meaningful when the positive and negative anchor populations are sampled from different units.
 
 Despite the significance of the problem for audit quality and regulatory oversight, no prior work has specifically addressed non-hand-signing detection in financial audit documents at scale with these methodological safeguards.
 Woodruff et al. [9] developed an automated pipeline for signature analysis in corporate filings for anti-money-laundering investigations, but their work focused on author clustering (grouping signatures by signer identity) rather than detecting reuse of a stored image.