gbanyan
da455791de
Address all 29 items from the fused reviewer report (Gemini 3.1 Pro + ChatGPT 5.5 + Opus 4.8): 3 fatal, 4 severe, arbitration A/B, 5 fusion-new, 15 minor. All new numbers computed from signature_analysis.db; nothing fabricated. Claim honesty (F1/F3/F4/F7/G3): - Retract all "139x the floor" comparisons; ICCR -> between-accountant specificity proxy throughout; state within-accountant FPR is not estimable and ICCR is not even a bound (anti-conservative direction). - Firm A reframed as quasi-positive known-positive benchmark (not blinded). - byte-identity recast as prevalence signal, not a recall/sanity check. - tunable -> single-direction conservativeness dial (no P-R frontier). New data analysis (verified, bit-reproducible via committed scripts): - F2/G1 (Sec V-B): 880-PDF imaging-pipeline audit (Table V) - plain scans 82% (2013) -> 1% (2021); producer strings name scanner hardware (Fuji Xerox D125 etc.); substrate transforms at 2020/21 = named confound. - F5 (Sec IV-C): four robustness checks - pool-size stratification, accountant-clustered bootstrap (gap 53.7pp [49.5,57.5]), firm+year FE logistic (B/C/D OR 0.06-0.12), leave-one-year-out (gap 53.1-54.9pp). - byte-identity era split: 30 scan-era (18 Firm A, pipeline-robust) vs 232 digital-era (detectability-inflated, hedged). - G5: archive-wide 888 expected chance HC flags [677,1098]. - M4: Figure 3 replaced with real 2D density (n=150,441). Structure/minor: abstract restructured (M1); operational definition (M2); interview disclaimer (M3); Threats to Validity subsection (M8); review protocol framed as design not evidence (M9); N reconciliations (M10/M11); Table II-c 2020-23 five-way (M12); Section refs, American spelling, notation table (M5/M13/M15); reference URLs verified (M14). Open (author-only): placeholders (M13), II-b/IV table merge (M15). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qn59FdF9JMyfFg3sjcUNNG
85 lines
3.8 KiB
Python
85 lines
3.8 KiB
Python
"""Figure 3 (real data version): 2D density of the two measures over the five-region scheme.
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Replaces the earlier schematic with the actual distribution, with axis ticks and the rule cuts.
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Reproduces from signature_analysis.db; Big-4, is_valid=1, both measures present."""
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from matplotlib.colors import LogNorm
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from matplotlib.patches import Rectangle
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import numpy as np
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import sqlite3
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DB = "/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db"
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BIG4 = ('勤業眾信聯合', '資誠聯合', '安侯建業聯合', '安永聯合')
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con = sqlite3.connect(DB)
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cur = con.cursor()
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cur.execute(f"""
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SELECT max_similarity_to_same_accountant, min_dhash_independent
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FROM signatures
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WHERE is_valid=1 AND max_similarity_to_same_accountant IS NOT NULL
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AND min_dhash_independent IS NOT NULL
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AND excel_firm IN ({','.join(['?']*4)})
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""", BIG4)
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rows = cur.fetchall()
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con.close()
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cos = np.array([r[0] for r in rows], dtype=float)
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dh = np.array([r[1] for r in rows], dtype=float)
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n = len(cos)
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LO, HI = 0.8547, 0.95
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DH1, DH2 = 5, 15
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xmin, xmax = 0.70, 1.002
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ymin, ymax = -0.5, 30
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ycap = 30 # display cap; values above are piled into the top row for visibility
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dh_disp = np.minimum(dh, ycap - 0.5)
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fig, ax = plt.subplots(figsize=(5.6, 4.4))
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# faint region tint behind the density
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ax.add_patch(Rectangle((xmin, ymin), LO - xmin, ymax - ymin, facecolor='#bdc3c7', alpha=0.12, zorder=0))
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ax.add_patch(Rectangle((LO, ymin), HI - LO, ymax - ymin, facecolor='#f7dc6f', alpha=0.12, zorder=0))
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ax.add_patch(Rectangle((HI, ymin), xmax - HI, DH1 - ymin, facecolor='#cb4335', alpha=0.14, zorder=0))
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ax.add_patch(Rectangle((HI, DH1), xmax - HI, DH2 - DH1, facecolor='#eb984e', alpha=0.14, zorder=0))
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ax.add_patch(Rectangle((HI, DH2), xmax - HI, ymax - DH2, facecolor='#aed6f1', alpha=0.14, zorder=0))
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# real 2D density (log counts)
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xedges = np.linspace(xmin, xmax, 90)
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yedges = np.arange(-0.5, ycap + 0.5, 1.0) # integer dHash bins
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H, xe, ye = np.histogram2d(cos, dh_disp, bins=[xedges, yedges])
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pcm = ax.pcolormesh(xe, ye, H.T, norm=LogNorm(vmin=1, vmax=H.max()),
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cmap='viridis', zorder=1, shading='flat')
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cb = fig.colorbar(pcm, ax=ax, pad=0.02)
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cb.set_label('signatures per cell (log scale)', fontsize=8)
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cb.ax.tick_params(labelsize=7)
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# cut lines
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ax.axvline(LO, color='gray', ls=':', lw=1.1, zorder=3)
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ax.axvline(HI, color='black', ls='--', lw=1.1, zorder=3)
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ax.plot([HI, xmax], [DH1, DH1], 'k--', lw=0.9, zorder=3)
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ax.plot([HI, xmax], [DH2, DH2], 'k--', lw=0.9, zorder=3)
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# region labels
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ax.text((xmin + LO) / 2, 24, 'LH', ha='center', fontsize=10, weight='bold', color='#34495e', zorder=4)
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ax.text((LO + HI) / 2, 24, 'UN', ha='center', fontsize=10, weight='bold', color='#7d6608', zorder=4)
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ax.text((HI + xmax) / 2, 2.2, 'HC', ha='center', fontsize=10, weight='bold', color='#cb4335', zorder=4)
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ax.text((HI + xmax) / 2, 9.7, 'MC', ha='center', fontsize=10, weight='bold', color='#a04000', zorder=4)
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ax.text((HI + xmax) / 2, 24, 'HSC', ha='center', fontsize=9, weight='bold', color='#21618c', zorder=4)
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ax.set_xlim(xmin, xmax)
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ax.set_ylim(ymin, ymax)
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ax.set_xticks([0.70, 0.75, 0.80, 0.8547, 0.90, 0.95, 1.00])
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ax.set_xticklabels(['0.70', '0.75', '0.80', '0.855', '0.90', '0.95', '1.00'], fontsize=7.5)
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ax.set_yticks([0, 5, 10, 15, 20, 25, 30])
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ax.set_yticklabels(['0', '5', '10', '15', '20', '25', '≥30'], fontsize=7.5)
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ax.set_xlabel('cosine similarity to same accountant (style)', fontsize=9)
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ax.set_ylabel('min dHash distance (structure)', fontsize=9)
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ax.set_title(f'Figure 3. Two-measure plane: real density over the five regions (Big-4, n={n:,})',
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fontsize=8.5)
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fig.tight_layout()
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out = '/Volumes/NV2/pdf_recognize/paper/v13_build/figures/fig3.png'
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fig.savefig(out, dpi=200, bbox_inches='tight')
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plt.close(fig)
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print(f'fig3 density OK: n={n:,}, dHash>=30 piled: {(dh>=ycap).sum()}, written {out}')
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