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_conclusion_v3.md

@@ -17,7 +17,7 @@ The Burgstahler-Dichev / McCrary test, by contrast, finds no significant transit
 The substantive reading is therefore narrower than "discrete behavior": *pixel-level output quality* is continuous and heavy-tailed, and *accountant-level aggregate behavior* is clustered with smooth cluster boundaries.
 
 Fourth, we introduced a *replication-dominated* calibration methodology---explicitly distinguishing replication-dominated from replication-pure calibration anchors and validating classification against a byte-level pixel-identity anchor (310 byte-identical signatures) paired with a $\sim$50,000-pair inter-CPA negative anchor.
-To break the circularity of using the calibration firm as its own validation reference, we split the firm's CPAs 70/30 at the CPA level and report post-hoc capture rates on the held-out fold with Wilson 95% confidence intervals.
+To document the within-firm sampling variance of using the calibration firm as its own validation reference, we split the firm's CPAs 70/30 at the CPA level and report capture rates on both folds with Wilson 95% confidence intervals; extreme rules agree across folds while rules in the operational 85-95% capture band differ by 1-5 percentage points, reflecting within-firm heterogeneity in replication intensity rather than generalization failure.
 This framing is internally consistent with all available evidence: the visual-inspection observation of pixel-identical signatures across unrelated audit engagements for the majority of calibration-firm partners; the 92.5% / 7.5% split in signature-level cosine thresholds; and, among the 171 calibration-firm CPAs with enough signatures to enter the accountant-level GMM (of 180 in total), the 139 / 32 split between the high-replication and middle-band clusters.
 
 An ablation study comparing ResNet-50, VGG-16 and EfficientNet-B0 confirmed that ResNet-50 offers the best balance of discriminative power, classification stability, and computational efficiency for this task.