{"id":"W2155628890","doi":"10.1109/ccece.2006.277447","title":"Towards a Measure of Biometric Information","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Entropy (arrow of time); Pattern recognition (psychology); Computer science; Artificial intelligence; Population; Feature (linguistics); Measure (data warehouse); Kullback–Leibler divergence; Facial recognition system; Gaussian; Information theory; Face (sociological concept); Mathematics; Data mining; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002677219,0.0000368672,0.00005777067,0.001149707,0.00002424652,0.00007824576,0.0003382439,0.00003407411,0.0000322681],"category_scores_gemma":[0.00005222073,0.00003063995,0.00003246856,0.005441375,0.00001564052,0.0006889676,0.00005028409,0.0000246652,0.00008358776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001674799,"about_ca_system_score_gemma":0.00003921143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003891007,"about_ca_topic_score_gemma":0.00000414172,"domain_scores_codex":[0.9993515,0.00001376583,0.0002016882,0.00006520384,0.0002950989,0.0000727195],"domain_scores_gemma":[0.999468,0.00001312565,0.00007719531,0.0002221118,0.0001987001,0.00002086827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00000107543,0.0000783331,0.00063438,0.00001883047,0.000004668769,1.819837e-7,0.0001286366,0.000003100132,0.0006097932,0.6370025,0.01226817,0.3492503],"study_design_scores_gemma":[0.001245439,0.0001128879,0.4338978,0.00001175283,0.00001057115,0.00001664719,0.00007268776,0.05484919,0.08985761,0.02101381,0.3984185,0.0004931389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003715873,0.00006574279,0.9489145,0.0004099602,0.0001515913,0.00005485111,0.000002187064,0.00008028973,0.04660501],"genre_scores_gemma":[0.9713247,0.000002632173,0.02835879,0.00007572278,0.000009014734,0.000001748978,0.000005753295,6.79687e-7,0.0002209186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9676089,"threshold_uncertainty_score":0.26144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266741953714987,"score_gpt":0.2174794084622204,"score_spread":0.2048119889250706,"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."}}