{"id":"W3011264165","doi":"10.1109/saupec/robmech/prasa48453.2020.9040973","title":"Dynamic fusion of human and machine decisions for efficient cost-sensitive biometric authentication","year":2020,"lang":"en","type":"article","venue":"2020 International SAUPEC/RobMech/PRASA Conference","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Authentication (law); Biometrics; Computer science; Protocol (science); Workforce; Database transaction; Quality (philosophy); Artificial intelligence; Computer security; Machine learning; Database","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":[0.0003526271,0.000193288,0.0002768635,0.0003299859,0.0001563623,0.0001956573,0.0009550726,0.00008622925,0.00006584633],"category_scores_gemma":[0.0005853002,0.0001867448,0.0001011978,0.0007236277,0.00008998667,0.0002141319,0.0004143973,0.0001333665,0.00003868401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007206032,"about_ca_system_score_gemma":0.0000838448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004079721,"about_ca_topic_score_gemma":0.00002565767,"domain_scores_codex":[0.997972,0.00008232915,0.000576919,0.0006047075,0.0005579627,0.0002061222],"domain_scores_gemma":[0.9980642,0.0003578998,0.000327679,0.0003492954,0.0007020494,0.0001988592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001280695,0.0006232417,0.001017198,0.0001212225,0.0002313128,0.00001215216,0.02884675,0.0000755472,0.2742696,0.6375867,0.0007521515,0.05633605],"study_design_scores_gemma":[0.0008539769,0.0001710881,0.007858238,0.00007791854,0.00002398499,0.00001185686,0.0001710847,0.9803166,0.00554968,0.002794256,0.001935673,0.0002355929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1542293,0.0000894521,0.8355001,0.008250984,0.0004206588,0.0008085183,0.0001881567,0.0001080301,0.0004048423],"genre_scores_gemma":[0.9934881,0.00005560813,0.00574252,0.0003118066,0.00003657684,0.00005531995,0.0001379677,0.00001268241,0.0001594053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9802411,"threshold_uncertainty_score":0.7615233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05135632591200593,"score_gpt":0.306033539076985,"score_spread":0.254677213164979,"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."}}