{"id":"W2110343412","doi":"","title":"An Optimal Score Fusion Strategy For a Multimodal Biometric Authentication System for Mobile Device","year":2010,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Computer science; Normalization (sociology); Authentication (law); Mobile device; Reliability (semiconductor); Artificial intelligence; Access control; Modal; Data mining; Machine learning; Computer security","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001417719,0.0002063971,0.0002922267,0.001431417,0.0008019913,0.0002054774,0.001821853,0.0003466659,0.00005372614],"category_scores_gemma":[0.000131226,0.000246853,0.0002327356,0.002528715,0.0001364832,0.001676902,0.0002186624,0.0002672551,0.00004420964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272453,"about_ca_system_score_gemma":0.0001672585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006395633,"about_ca_topic_score_gemma":0.0000922249,"domain_scores_codex":[0.9980083,0.000105457,0.0002847545,0.0007509488,0.0004509904,0.000399596],"domain_scores_gemma":[0.9973835,0.000216977,0.0003697467,0.000957992,0.0007815438,0.0002901909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008917423,0.001949788,0.01674843,0.001239422,0.0002042518,0.00001220825,0.006667732,0.0002865342,0.7962731,0.03455067,0.0004020508,0.1407741],"study_design_scores_gemma":[0.01125749,0.002409989,0.4180362,0.0001614658,0.0003748849,0.00007824375,0.006306594,0.4432941,0.09076623,0.000949188,0.02429205,0.002073494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.761344,0.00004949803,0.2366703,0.0001111076,0.000463091,0.0009807472,0.0001671914,0.0001600925,0.00005390602],"genre_scores_gemma":[0.9195933,0.000003216936,0.07982361,0.00001655309,0.0000626402,0.00001143294,0.0001666838,0.00001582073,0.0003067169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7055068,"threshold_uncertainty_score":0.9999984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03621043151043585,"score_gpt":0.2749491212342444,"score_spread":0.2387386897238086,"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."}}