{"id":"W4319165505","doi":"10.1016/j.bushor.2023.02.001","title":"Can behavioral biometrics make everyone happy?","year":2023,"lang":"en","type":"article","venue":"Business Horizons","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Queen's University","funders":"","keywords":"Biometrics; Internet privacy; Transparency (behavior); Business; Behavioral pattern; Identity (music); Focus (optics); Iris recognition; Work (physics); Marketing; Computer science; Computer security; Engineering","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":[0.0002403483,0.0001374766,0.0001717298,0.0008283841,0.0001670979,0.0002656829,0.0008382717,0.0000783662,0.00001572989],"category_scores_gemma":[0.00008326924,0.0001343739,0.00005620623,0.009198866,0.0000434854,0.0002326978,0.0002692095,0.00008151415,0.0005810506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004208489,"about_ca_system_score_gemma":0.0001105034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002567975,"about_ca_topic_score_gemma":0.000104641,"domain_scores_codex":[0.9985775,0.00004219324,0.0002423147,0.0003621938,0.0004257218,0.0003500968],"domain_scores_gemma":[0.9987912,0.0000577384,0.00007444162,0.000699971,0.0002499789,0.0001266959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002612464,0.002966146,0.06416944,0.0006268141,0.0002289221,0.0009794051,0.08205206,0.00005997147,0.01870133,0.4165083,0.1592493,0.2544322],"study_design_scores_gemma":[0.001120722,0.0001788259,0.6239979,0.0001156868,0.00005700277,0.0001005343,0.0005287851,0.0564709,0.0009420924,0.002960288,0.3121713,0.00135595],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7822747,0.0004007546,0.1832547,0.01755251,0.008990593,0.0009712494,0.0001167351,0.004390449,0.002048223],"genre_scores_gemma":[0.9965006,0.00003303585,0.0005793733,0.00007957033,0.0001052628,0.0000354055,0.00003495716,0.00001589274,0.002615936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5598285,"threshold_uncertainty_score":0.7468423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0454777134119544,"score_gpt":0.2833770687518083,"score_spread":0.2378993553398539,"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."}}