{"id":"W1511102755","doi":"10.1109/isba.2015.7126360","title":"Prior resemblance probability of users for multimodal biometrics rank fusion","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Rank (graph theory); Artificial intelligence; Face (sociological concept); Identity (music); Key (lock); Pattern recognition (psychology); Machine learning; Mathematics; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00201298,0.0004960102,0.000589357,0.0007720381,0.0005882456,0.0007873729,0.0008439352,0.0006126279,0.002659629],"category_scores_gemma":[0.007679525,0.0003327863,0.000620503,0.0005443951,0.0007436281,0.001801134,0.001261472,0.0009260213,0.0009872537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000679495,"about_ca_system_score_gemma":0.0005999242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181508,"about_ca_topic_score_gemma":0.001330601,"domain_scores_codex":[0.9980556,0.0005153175,0.00009316083,0.0004004595,0.0007986773,0.0001367871],"domain_scores_gemma":[0.9967655,0.001144667,0.000311402,0.0008989943,0.0007027665,0.0001767244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00141457,0.0005059767,0.009198778,0.0001632977,0.0001072252,0.0002694023,0.0003473008,0.2472352,0.1272297,0.01995764,0.001396225,0.5921748],"study_design_scores_gemma":[0.00002287343,0.0004314537,0.008303327,0.00001670297,0.00004678624,0.0004050404,0.00005512609,0.9324499,0.05038686,0.005977316,0.001828703,0.00007584097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1033709,0.0002314515,0.8933097,0.0001090186,0.00003178993,0.00007056167,0.00006287723,0.0006581674,0.002155725],"genre_scores_gemma":[0.9274895,0.0001277636,0.07035661,0.00003738372,0.00003430765,0.00005436395,0.00008131334,0.00005031662,0.001768641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002659629,"threshold_uncertainty_score":0.01064581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09630601291370634,"score_gpt":0.3167083030286391,"score_spread":0.2204022901149328,"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."}}