{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00109529,0.0007747082,0.0008577564,0.0008412965,0.0005908686,0.0008254266,0.0008808617,0.00076585,0.001884867],"category_scores_gemma":[0.001915445,0.0002453849,0.0006735335,0.0006447887,0.0005380863,0.0009084655,0.0008327374,0.0004279293,0.000710492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007595012,"about_ca_system_score_gemma":0.0008320683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002705489,"about_ca_topic_score_gemma":0.002834846,"domain_scores_codex":[0.9990937,0.0001759875,0.00006028083,0.0002171152,0.0003750415,0.00007796866],"domain_scores_gemma":[0.9996678,0.00006442882,0.00004073605,0.0000265738,0.0001788899,0.00002157451],"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":[0.0004593154,0.0001731239,0.001313557,0.0001197903,0.0001227088,0.0001581195,0.0002678793,0.1585042,0.124668,0.01639379,0.001866925,0.6959525],"study_design_scores_gemma":[0.00002414848,0.0002955724,0.001308682,0.00001336588,0.00006700554,0.0001678102,0.00004518726,0.9671887,0.02518361,0.003882361,0.001783511,0.00004018758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01876311,0.0001140047,0.9797413,0.0000676801,0.00002267172,0.00006565312,0.00001542607,0.0002385015,0.0009716056],"genre_scores_gemma":[0.5366284,0.0001532846,0.4591773,0.00009144544,0.00003203709,0.0002088868,0.000083656,0.00003650056,0.003588477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002705489,"threshold_uncertainty_score":0.006305516,"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."}}