{"id":"W1995909705","doi":"10.1109/icmlc.2009.5212173","title":"An automation for robust design of multimodal biometric systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Computer science; Normalization (sociology); Automation; Scalability; Data mining; Modalities; Artificial intelligence; Set (abstract data type); Matching (statistics); Standard deviation; Machine learning; Robustness (evolution); Pattern recognition (psychology); Database; Engineering; Mathematics","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":[0.0006121531,0.0000585612,0.0001032638,0.001047561,0.0000538829,0.0001406406,0.0004766345,0.00005609484,0.000004747456],"category_scores_gemma":[0.00007203445,0.00005099389,0.00003226493,0.003014997,0.000011358,0.000441124,0.00001038158,0.0000208136,0.000008494554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002647886,"about_ca_system_score_gemma":0.00003295005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004661131,"about_ca_topic_score_gemma":2.862238e-7,"domain_scores_codex":[0.999196,0.00005778321,0.0002353422,0.0002004312,0.0001953519,0.0001150955],"domain_scores_gemma":[0.9991826,0.00009290428,0.0001080678,0.0003475948,0.0002123824,0.00005640894],"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":[0.00001835783,0.001190959,0.0001081791,0.00007516731,0.00002522669,8.807302e-7,0.0007523153,0.01654411,0.03417699,0.296998,0.007655392,0.6424544],"study_design_scores_gemma":[0.0002115907,0.0001632347,0.004635088,0.000002612784,0.000001711789,0.000001521417,0.00001314985,0.9914854,0.002830433,0.0002924362,0.0002942929,0.00006851755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008169218,0.0001001622,0.9980178,0.000167994,0.0002584198,0.0003557754,0.000002947806,0.0001708249,0.0001090871],"genre_scores_gemma":[0.6061159,0.000003839416,0.3937418,0.00003940548,0.00001651263,0.000006526371,0.00000503626,0.000001421435,0.00006954877],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9749413,"threshold_uncertainty_score":0.2079471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06149167539863303,"score_gpt":0.292855998788669,"score_spread":0.231364323390036,"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."}}