{"id":"W1998227249","doi":"10.1007/s10044-008-0120-3","title":"Towards a measure of biometric feature information","year":2008,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Biometrics; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Entropy (arrow of time); Linear discriminant analysis; Population; Measure (data warehouse); Computer science; Mathematics; Facial recognition system; Data mining","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":[0.0001684745,0.0000657441,0.000150259,0.001581583,0.0001329731,0.00005939566,0.0003199319,0.00004875868,0.00001535438],"category_scores_gemma":[0.00001666413,0.00005769704,0.00009932824,0.01030118,0.00004535037,0.0002932753,0.00006605876,0.0000549798,0.00002191892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001213517,"about_ca_system_score_gemma":0.00002518917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001618659,"about_ca_topic_score_gemma":0.00001161682,"domain_scores_codex":[0.9992718,0.00002141911,0.0002101657,0.0001555275,0.0002578423,0.00008328746],"domain_scores_gemma":[0.9991549,0.00002182924,0.0001521697,0.0003975435,0.0002100247,0.00006356119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[7.590818e-7,0.0001186552,0.03968619,0.00002771546,0.0002725074,2.856227e-7,0.0006927263,0.000006759871,0.0003024919,0.008092633,0.0009739862,0.9498253],"study_design_scores_gemma":[0.0002877578,0.00001927579,0.902093,0.000003338874,0.000234693,0.000012088,0.00006039111,0.02037959,0.002903469,0.0004012797,0.07336769,0.0002374128],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006262507,0.000230595,0.9920004,0.0006972856,0.00001229779,0.0001121866,0.00002758235,0.00003598662,0.0006211286],"genre_scores_gemma":[0.9951513,0.0001697979,0.00435792,0.0001492971,0.00001260425,0.00004099857,0.00005541165,0.000001341819,0.00006128046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9888889,"threshold_uncertainty_score":0.4949376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859572080059288,"score_gpt":0.2433341003508717,"score_spread":0.2247383795502788,"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."}}