{"id":"W2541439781","doi":"10.1109/bcc.2006.4341618","title":"Measuring Biometric Sample Quality in Terms of Biometric Information","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Sample (material); Quality (philosophy); Artificial intelligence; Pattern recognition (psychology)","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001268254,0.00008475067,0.0001655291,0.009217315,0.00004010786,0.0001432828,0.0006029416,0.00006927747,0.00002450668],"category_scores_gemma":[0.0005437046,0.00007726374,0.00005764899,0.03106667,0.00003314227,0.001223867,0.000129873,0.00006368696,0.0000528693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008793061,"about_ca_system_score_gemma":0.00002690401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004958378,"about_ca_topic_score_gemma":0.00008096177,"domain_scores_codex":[0.9983557,0.00006921568,0.0006944737,0.0001750712,0.0005140837,0.0001914287],"domain_scores_gemma":[0.9989312,0.0002451749,0.0002339467,0.0004259348,0.0001221247,0.00004158132],"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":[0.000007546742,0.0005971479,0.2196306,0.0001761754,0.00001285639,6.085604e-7,0.0003813291,0.00003989357,0.003371899,0.3849964,0.001530657,0.3892549],"study_design_scores_gemma":[0.0005210841,0.00002282293,0.9630486,0.000005946755,0.000001528019,0.000001525888,0.00001873135,0.007382496,0.01322514,0.00762613,0.007960947,0.0001850035],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08619495,0.00006783918,0.9012295,0.0001575925,0.0002415905,0.0001601281,0.00001409854,0.0001077508,0.01182653],"genre_scores_gemma":[0.9714569,0.000008905982,0.02839837,0.00004720428,0.00001014913,0.000004900674,0.00002046436,0.000001833087,0.00005130242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8852619,"threshold_uncertainty_score":0.9895285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05464484584472577,"score_gpt":0.2706097510212528,"score_spread":0.215964905176527,"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."}}