{"id":"W4205216148","doi":"10.1109/access.2022.3144145","title":"Template Aging in Multi-Modal Social Behavioral Biometrics","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":8,"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":"Computer science; Biometrics; Authentication (law); Modal; Feature (linguistics); Behavioral pattern; Identification (biology); Human–computer interaction; Data mining; Artificial intelligence; Machine learning; Computer security","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":[0.0006657427,0.00009497307,0.0001251017,0.0005536423,0.0004575859,0.0002404079,0.001372282,0.00004218681,0.00005255093],"category_scores_gemma":[0.0000136446,0.0001085266,0.0000514195,0.002839156,0.00002190742,0.0006411342,0.0006806162,0.0003404309,0.00001534041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001519262,"about_ca_system_score_gemma":0.00006946569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002496822,"about_ca_topic_score_gemma":0.0000240694,"domain_scores_codex":[0.9986811,0.0001508088,0.0002169546,0.0003037231,0.0003324378,0.0003149183],"domain_scores_gemma":[0.9995936,0.00004482898,0.0000915153,0.0001830622,0.00003417369,0.00005278522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007507174,0.002165446,0.6471059,0.00009446152,0.00004678361,0.00108732,0.02090167,0.007371452,0.01130597,0.02277274,0.004860137,0.2822131],"study_design_scores_gemma":[0.00824921,0.0004033571,0.4486647,0.00004051994,0.00004290566,0.0001455241,0.00116627,0.4440524,0.05263065,0.007291051,0.03414112,0.003172351],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6525546,0.00005544077,0.344808,0.0006958664,0.001505332,0.0001299532,0.00001638942,0.0001504052,0.00008409697],"genre_scores_gemma":[0.997001,0.000001357721,0.002514251,0.0002838077,0.00005248078,0.00003426569,0.000006948337,0.000007677349,0.0000982378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4366809,"threshold_uncertainty_score":0.4425588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2369556752068739,"score_gpt":0.4330654753839694,"score_spread":0.1961098001770956,"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."}}