{"id":"W1986897909","doi":"10.1109/ccece.2006.277715","title":"Human Vs. Automatic Measurement of Biometric Sample Quality","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Face (sociological concept); Artificial intelligence; Iris recognition; Image quality; Computer science; IRIS (biosensor); Quality (philosophy); Facial recognition system; Pattern recognition (psychology); Quality Score; Identification (biology); Sample (material); Image (mathematics); Gold standard (test); Computer vision; Mathematics; Statistics; Engineering","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":[0.001493224,0.00007570907,0.0001579887,0.0009796377,0.00008317515,0.00008959355,0.000611883,0.00004077583,0.0001136485],"category_scores_gemma":[0.0001765318,0.00006604741,0.0000711882,0.004601288,0.00004063094,0.0001594597,0.00009901018,0.00003917963,0.00004102286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007488385,"about_ca_system_score_gemma":0.00004084911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003592743,"about_ca_topic_score_gemma":0.0001132982,"domain_scores_codex":[0.998263,0.00009258187,0.0004718978,0.0002314848,0.000789503,0.0001515345],"domain_scores_gemma":[0.9988728,0.00007062079,0.0001730297,0.0005666208,0.0002723331,0.00004464336],"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":[9.646208e-7,0.001018457,0.009669746,0.0001567469,0.00002970242,5.014522e-7,0.0001215205,0.000003608457,0.01857492,0.892452,0.02392658,0.05404522],"study_design_scores_gemma":[0.0005361353,0.00006913606,0.9299582,0.000009420726,0.000008044499,0.000001345617,0.00001690957,0.009042445,0.0288715,0.01889926,0.01231297,0.0002746484],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0568094,0.0001179402,0.9349355,0.0003824818,0.0001968985,0.0001562037,0.000005858263,0.0002423598,0.007153407],"genre_scores_gemma":[0.9622142,8.367233e-7,0.03748092,0.00005531135,0.00001421049,0.000004420035,0.000004015419,0.000002481347,0.0002236542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9202884,"threshold_uncertainty_score":0.5431177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08120569486051317,"score_gpt":0.3149023494255307,"score_spread":0.2336966545650175,"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."}}