{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006243167,0.0007958891,0.001333464,0.004770765,0.0006111122,0.003188525,0.001443011,0.001617018,0.002058487],"category_scores_gemma":[0.03582572,0.0005508882,0.0008742982,0.003963415,0.002552293,0.005612351,0.002689695,0.001352189,0.0007249273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359927,"about_ca_system_score_gemma":0.0004327612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522064,"about_ca_topic_score_gemma":0.0009231713,"domain_scores_codex":[0.9882872,0.00199581,0.0007487151,0.001894652,0.006698449,0.0003752171],"domain_scores_gemma":[0.9735207,0.01348724,0.004434448,0.004613754,0.003566331,0.0003774396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001503209,0.00038161,0.1427046,0.001287079,0.001002086,0.0006742961,0.002383302,0.1120808,0.1372535,0.06783415,0.002937986,0.5299574],"study_design_scores_gemma":[0.000104164,0.00228103,0.3577991,0.0006025601,0.0006611804,0.004312819,0.001450831,0.3559444,0.1437646,0.1106386,0.02163572,0.0008048494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1449553,0.001926192,0.846051,0.0003572591,0.0001362586,0.0001617476,0.0008675196,0.0006518652,0.00489301],"genre_scores_gemma":[0.805883,0.0008868872,0.189918,0.000289727,0.0001410542,0.0001865818,0.0008244224,0.0001816646,0.001688675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006243167,"threshold_uncertainty_score":0.03301746,"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."}}