Paper A v3.19.1: address codex partner-redpen audit residual ("upper bound" wording)

Codex GPT-5.5 cross-verified the Gemini partner red-pen audit
(paper/codex_partner_redpen_audit_v3_19_0.md) and downgraded item (j) --
the BIC strict-3-component upper-bound framing -- from RESOLVED to
IMPROVED, because the "upper bound" wording the partner originally
red-circled in v3.17 still survived in two methodology sentences and one
Table VI row label, even though Section IV-D.3 had been retitled
"A Forced Fit" in v3.18.

This commit closes that residual:

- Methodology III-I.2: "the 2-component crossing should be treated as
  an upper bound rather than a definitive cut" -> "we report the
  resulting crossing only as a forced-fit descriptive reference and do
  not use it as an operational threshold".
- Methodology III-I.4: "should be read as an upper bound rather than a
  definitive cut" -> "reported only as a descriptive reference rather
  than as an operational threshold".
- Table VI row "0.973 (signature-level Beta/KDE upper bound)" relabelled
  to "0.973 (signature-level Beta/KDE forced-fit reference)" to match
  the IV-D.3 "Forced Fit" framing.
- reference_verification_v3.md header updated so the [5] entry reads as
  an audit trail of a fix already applied (v3.18 reference list reflects
  every correction) rather than as an active major problem.
- Rebuild Paper_A_IEEE_Access_Draft_v3.docx.

Also commits the codex partner-redpen audit artifact so the disagreement
trail with Gemini is preserved.

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

paper/paper_a_methodology_v3.md

@@ -208,7 +208,7 @@ As a robustness check against the Beta parametric form we fit a parallel two-com
 White's [41] quasi-MLE consistency result justifies interpreting the logit-Gaussian estimates as asymptotic approximations to the best Gaussian-family fit under misspecification; we use the cross-check between Beta and logit-Gaussian crossings as a diagnostic of parametric-form sensitivity rather than as a guarantee of distributional recovery.
 
 We fit 2- and 3-component variants of each mixture and report BIC for model selection.
-When BIC prefers the 3-component fit, the 2-component assumption itself is a forced fit, and the Bayes-optimal threshold derived from the 2-component crossing should be treated as an upper bound rather than a definitive cut.
+When BIC prefers the 3-component fit, the 2-component assumption itself is a forced fit; we report the resulting crossing only as a forced-fit descriptive reference and do not use it as an operational threshold.
 
 ### 3) Density-Smoothness Diagnostic: Burgstahler-Dichev / McCrary
 
@@ -228,7 +228,7 @@ The two threshold estimators rest on decreasing-in-strength assumptions: the KDE
 If the two estimated thresholds were to differ by less than a practically meaningful margin and the BD/McCrary procedure were to identify a sharp transition at the same level, that pattern would constitute convergent evidence for a clean two-mechanism boundary at that location.
 
 This is *not* the pattern we observe at the per-signature level.
-The two threshold estimators yield crossings spread across a wide range (Section IV-D); the BIC clearly prefers a 3-component over a 2-component Beta fit, indicating that the 2-component crossing is a forced fit and should be read as an upper bound rather than a definitive cut; and the BD/McCrary procedure locates its candidate transition *inside* the non-hand-signed mode rather than between modes (Appendix A).
+The two threshold estimators yield crossings spread across a wide range (Section IV-D); the BIC clearly prefers a 3-component over a 2-component Beta fit, indicating that the 2-component crossing is a forced fit reported only as a descriptive reference rather than as an operational threshold; and the BD/McCrary procedure locates its candidate transition *inside* the non-hand-signed mode rather than between modes (Appendix A).
 We interpret this jointly as evidence that per-signature similarity is a continuous quality spectrum rather than a clean two-mechanism mixture, and we accordingly anchor the operational classifier's cosine cut on whole-sample Firm A percentile heuristics (Section III-K) rather than on a mixture-fit crossing.
 
 ## J. Pixel-Identity, Inter-CPA, and Held-Out Firm A Validation (No Manual Annotation)