{"id":"W11477303","doi":"10.1201/9781315220444","title":"Biometric Inverse Problems","year":2018,"lang":"en","type":"book","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Fingerprint (computing); Artificial intelligence; Orientation (vector space); Iris recognition; Pattern recognition (psychology); Computer vision; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000437359,0.0002050904,0.0002219782,0.003345888,0.00009045181,0.0003522951,0.001790285,0.000340855,0.0007992563],"category_scores_gemma":[0.00007133892,0.0001801465,0.0001230769,0.003829473,0.0001125829,0.0002591886,0.0004597022,0.0001953997,0.008164179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001790312,"about_ca_system_score_gemma":0.0003886417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001875809,"about_ca_topic_score_gemma":0.0000151819,"domain_scores_codex":[0.9982767,0.00003310879,0.0003143412,0.0006125314,0.0005249808,0.000238328],"domain_scores_gemma":[0.9982107,0.00006758138,0.0001974309,0.001106707,0.0002724995,0.0001450749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[1.629691e-7,0.00002444891,0.000002034219,0.00003332837,0.00001642357,0.000002189189,0.00005392179,2.930347e-8,0.000003929258,0.08689782,0.8987942,0.01417153],"study_design_scores_gemma":[0.00009182432,0.00003468709,0.00002301332,0.00001401978,0.000005960032,0.000004671828,7.477323e-7,0.001665262,0.00003511234,0.01656037,0.9813221,0.0002422787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[9.765181e-7,0.0002510117,0.2291819,0.0002382854,0.001273575,0.0002088455,0.000007588381,0.0003425053,0.7684953],"genre_scores_gemma":[0.0000250841,0.00009363605,0.01956892,0.0005207472,0.0001959981,0.000008295797,0.00004679157,0.00001335077,0.9795272],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2110319,"threshold_uncertainty_score":0.9926081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.036873764057741,"score_gpt":0.2401950349587881,"score_spread":0.2033212709010471,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}