{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001335455,0.00008659104,0.0001351532,0.0001225797,0.0001733426,0.0004502115,0.0007471325,0.00006144289,0.000004312668],"category_scores_gemma":[0.00001963494,0.00008183129,0.00004710074,0.000749731,0.00002893203,0.0005225082,0.000163126,0.00005724819,0.000009548413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001253932,"about_ca_system_score_gemma":0.00002969967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003861907,"about_ca_topic_score_gemma":0.000005706947,"domain_scores_codex":[0.9991457,0.00002483659,0.0001728881,0.0002866748,0.0002128713,0.0001570573],"domain_scores_gemma":[0.9995496,0.00003775784,0.00007313795,0.0001348615,0.0001006262,0.0001040233],"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":[0.00007479144,0.000617563,0.01768622,0.0005685913,0.00007324606,0.00002774667,0.1657285,0.000002372123,0.0138949,0.01035441,0.03913462,0.751837],"study_design_scores_gemma":[0.007394997,0.0007343576,0.07276783,0.00006499187,0.0001143019,0.00002743192,0.0004598309,0.824803,0.02862453,0.005590728,0.05773742,0.001680574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3444866,0.00007267061,0.6507428,0.003510925,0.0006695693,0.0003422461,0.00003754074,0.0001121038,0.00002556092],"genre_scores_gemma":[0.9970428,0.000006439893,0.001978681,0.0006616063,0.0002318963,0.00002365449,0.00000637614,0.000007442292,0.00004110897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8248006,"threshold_uncertainty_score":0.43414,"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."}}