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_impact_statement_v3.md

@@ -1,9 +1,21 @@
-# Impact Statement
+<!--
+ARCHIVED. Not part of the IEEE Access submission.
 
-<!-- 100-150 words. Non-specialist readable. No jargon. Specific, not vague. -->
+IEEE Access Regular Papers do not include a separate Impact Statement
+section. The text below is retained for possible reuse in a cover
+letter, grant report, or non-IEEE venue. It is excluded from the
+assembled paper by export_v3.py.
+
+If reused, note that the wording "distinguishes genuinely hand-signed
+signatures from reproduced ones" overstates what a five-way confidence
+classifier without a fully labeled test set establishes; soften before
+external use.
+-->
+
+# Impact Statement (archived; not in IEEE Access submission)
 
 Auditor signatures on financial reports are a key safeguard of corporate accountability.
 When the signature on an audit report is produced by reproducing a stored image instead of by the partner's own hand---whether through an administrative stamping workflow or a firm-level electronic signing system---this safeguard is weakened, yet detecting the practice through manual inspection is infeasible at the scale of modern financial markets.
-We developed an artificial intelligence system that automatically extracts and analyzes signatures from over 90,000 audit reports spanning a decade of filings by publicly listed companies in Taiwan.
-By combining deep-learning visual features with perceptual hashing and three methodologically distinct threshold-selection methods, the system distinguishes genuinely hand-signed signatures from reproduced ones and quantifies how this practice varies across firms and over time.
-After further validation, the technology could support financial regulators in automating signature-authenticity screening at national scale.
+We developed a pipeline that automatically extracts and analyzes signatures from over 90,000 audit reports spanning a decade of filings by publicly listed companies in Taiwan.
+Combining deep-learning visual features with perceptual hashing and three methodologically distinct threshold-selection methods, the system stratifies signatures into a five-way confidence-graded classification and quantifies how the practice varies across firms and over time.
+After further validation, the technology could support financial regulators in screening signature authenticity at national scale.