{"id":"W3080092958","doi":"10.1109/access.2020.3018958","title":"Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Rank (graph theory); Information fusion; Sensor fusion; Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics","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":[0.002564523,0.001049964,0.001288364,0.002210246,0.0006817062,0.001385363,0.001060795,0.0009394931,0.001929187],"category_scores_gemma":[0.006311753,0.0002087153,0.00106073,0.001759813,0.0004480816,0.001426478,0.001123172,0.00101364,0.001397407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008652874,"about_ca_system_score_gemma":0.0008846245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003681233,"about_ca_topic_score_gemma":0.002914438,"domain_scores_codex":[0.9974002,0.0005217048,0.0001832915,0.0004270178,0.001240397,0.000227471],"domain_scores_gemma":[0.998068,0.0005337498,0.0002720107,0.0002352028,0.000815303,0.00007579616],"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":[0.0005224994,0.0003158197,0.007107421,0.00009779898,0.000201777,0.00008362924,0.0001459131,0.08166059,0.02244665,0.005141565,0.002460879,0.8798154],"study_design_scores_gemma":[0.00001800068,0.0003246718,0.005248843,0.00001767724,0.00006344849,0.0001331645,0.00007431759,0.9753672,0.01241895,0.004339612,0.001951684,0.00004241012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08037155,0.0008663989,0.9142348,0.0002351403,0.0001338883,0.0001306816,0.0002353705,0.001384421,0.002407752],"genre_scores_gemma":[0.7145538,0.0004088464,0.279326,0.000143341,0.00009205762,0.0001728136,0.0007670946,0.00007940208,0.004456675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003681233,"threshold_uncertainty_score":0.01356262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3427442017382644,"score_gpt":0.3969804106548913,"score_spread":0.05423620891662684,"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."}}