Paper A v3.3: apply codex v3.2 peer-review fixes

Codex (gpt-5.4) second-round review recommended 'minor revision'. This
commit addresses all issues flagged in that review.

## Structural fixes

- dHash calibration inconsistency (codex #1, most important):
  Clarified in Section III-L that the <=5 and <=15 dHash cutoffs come
  from the whole-sample Firm A cosine-conditional dHash distribution
  (median=5, P95=15), not from the calibration-fold independent-minimum
  dHash distribution (median=2, P95=9) which we report elsewhere as
  descriptive anchors. Added explicit note about the two dHash
  conventions and their relationship.

- Section IV-H framing (codex #2):
  Renamed "Firm A Benchmark Validation: Threshold-Independent Evidence"
  to "Additional Firm A Benchmark Validation" and clarified in the
  section intro that H.1 uses a fixed 0.95 cutoff, H.2 is fully
  threshold-free, H.3 uses the calibrated classifier. H.3's concluding
  sentence now says "the substantive evidence lies in the cross-firm
  gap" rather than claiming the test is threshold-free.

- Table XVI 93,979 typo fixed (codex #3):
  Corrected to 84,354 total (83,970 same-firm + 384 mixed-firm).

- Held-out Firm A denominator 124+54=178 vs 180 (codex #4):
  Added explicit note that 2 CPAs were excluded due to disambiguation
  ties in the CPA registry.

- Table VIII duplication (codex #5):
  Removed the duplicate accountant-level-only Table VIII comment; the
  comprehensive cross-level Table VIII subsumes it. Text now says
  "accountant-level rows of Table VIII (below)".

- Anonymization broken in Tables XIV-XVI (codex #6):
  Replaced "Deloitte"/"KPMG"/"PwC"/"EY" with "Firm A"/"Firm B"/"Firm C"/
  "Firm D" across Tables XIV, XV, XVI. Table and caption language
  updated accordingly.

- Table X unit mismatch (codex #7):
  Dropped precision, recall, F1 columns. Table now reports FAR
  (against the inter-CPA negative anchor) with Wilson 95% CIs and FRR
  (against the byte-identical positive anchor). III-K and IV-G.1 text
  updated to justify the change.

## Sentence-level fixes

- "three independent statistical methods" in Methodology III-A ->
  "three methodologically distinct statistical methods".
- "three independent methods" in Conclusion -> "three methodologically
  distinct methods".
- Abstract "~0.006 converging" now explicitly acknowledges that
  BD/McCrary produces no significant accountant-level discontinuity.
- Conclusion ditto.
- Discussion limitation sentence "BD/McCrary should be interpreted at
  the accountant level for threshold-setting purposes" rewritten to
  reflect v3.3 result that BD/McCrary is a diagnostic, not a threshold
  estimator, at the accountant level.
- III-H "two analyses" -> "three analyses" (H.1 longitudinal stability,
  H.2 partner ranking, H.3 intra-report consistency).
- Related Work White 1982 overclaim rewritten: "consistent estimators
  of the pseudo-true parameter that minimizes KL divergence" replaces
  "guarantees asymptotic recovery".
- III-J "behavior is close to discrete" -> "practice is clustered".
- IV-D.2 pivot sentence "discreteness of individual behavior yields
  bimodality" -> "aggregation over signatures reveals clustered (though
  not sharply discrete) patterns".

Target journal remains IEEE Access. Output:
Paper_A_IEEE_Access_Draft_v3.docx (395 KB).

Codex v3.2 review saved to paper/codex_review_gpt54_v3_2.md.

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

paper/paper_a_conclusion_v3.md

@@ -3,7 +3,7 @@
 ## Conclusion
 
 We have presented an end-to-end AI pipeline for detecting non-hand-signed auditor signatures in financial audit reports at scale.
-Applied to 90,282 audit reports from Taiwanese publicly listed companies spanning 2013--2023, our system extracted and analyzed 182,328 CPA signatures using a combination of VLM-based page identification, YOLO-based signature detection, deep feature extraction, and dual-descriptor similarity verification, with threshold selection placed on a statistically principled footing through three independent methods applied at two analysis levels.
+Applied to 90,282 audit reports from Taiwanese publicly listed companies spanning 2013--2023, our system extracted and analyzed 182,328 CPA signatures using a combination of VLM-based page identification, YOLO-based signature detection, deep feature extraction, and dual-descriptor similarity verification, with threshold selection placed on a statistically principled footing through three methodologically distinct methods applied at two analysis levels.
 
 Our contributions are fourfold.
 
@@ -12,8 +12,8 @@ First, we argued that non-hand-signing detection is a distinct problem from sign
 Second, we showed that combining cosine similarity of deep embeddings with difference hashing is essential for meaningful classification---among 71,656 documents with high feature-level similarity, the dual-descriptor framework revealed that only 41% exhibit converging structural evidence of non-hand-signing while 7% show no structural corroboration despite near-identical feature-level appearance, demonstrating that a single-descriptor approach conflates style consistency with image reproduction.
 
 Third, we introduced a three-method threshold framework combining KDE antimode (with a Hartigan unimodality test), Burgstahler-Dichev / McCrary discontinuity, and EM-fitted Beta mixture (with a logit-Gaussian robustness check).
-Applied at both the signature and accountant levels, this framework surfaced an informative structural asymmetry: at the per-signature level the distribution is a continuous quality spectrum for which no two-mechanism mixture provides a good fit, whereas at the per-accountant level BIC cleanly selects a three-component mixture and the three 1D methods agree within $\sim 0.006$ at cosine $\approx 0.975$.
-The Burgstahler-Dichev / McCrary test finds no significant transition at the accountant level, consistent with clustered but smoothly mixed rather than sharply discrete accountant-level behavior.
+Applied at both the signature and accountant levels, this framework surfaced an informative structural asymmetry: at the per-signature level the distribution is a continuous quality spectrum for which no two-mechanism mixture provides a good fit, whereas at the per-accountant level BIC cleanly selects a three-component mixture and the KDE antimode together with the Beta-mixture and logit-Gaussian estimators agree within $\sim 0.006$ at cosine $\approx 0.975$.
+The Burgstahler-Dichev / McCrary test, by contrast, finds no significant transition at the accountant level, consistent with clustered but smoothly mixed rather than sharply discrete accountant-level heterogeneity.
 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.