{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003821878,0.0002958299,0.0003531608,0.0009022983,0.0002309121,0.001125435,0.0004255163,0.0007189624,0.002579975],"category_scores_gemma":[0.01834136,0.000189849,0.0002846229,0.0007525901,0.0007313068,0.0009416052,0.00071668,0.0003782464,0.0006911956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003227259,"about_ca_system_score_gemma":0.0001949772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008144695,"about_ca_topic_score_gemma":0.0008913417,"domain_scores_codex":[0.9943718,0.00209434,0.000260037,0.0007819373,0.002273037,0.0002188119],"domain_scores_gemma":[0.9877571,0.00581296,0.00158235,0.001700465,0.002857533,0.0002896517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003769587,0.0004519651,0.4597845,0.0007621479,0.0007060127,0.0002135525,0.001405526,0.006864387,0.3008493,0.003396023,0.001484095,0.220313],"study_design_scores_gemma":[0.00008787483,0.002653403,0.8397881,0.000100896,0.0001915124,0.00136836,0.000403257,0.0287545,0.1213565,0.002586693,0.002581237,0.0001276349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216359,0.00161804,0.07012582,0.0001602233,0.0001021,0.0001497203,0.0004364937,0.0002551505,0.005516588],"genre_scores_gemma":[0.9880433,0.0002113854,0.01032326,0.00008035752,0.00003859794,0.0000570582,0.0002173091,0.00003782003,0.0009908706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003821878,"threshold_uncertainty_score":0.02021229,"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."}}