{"id":"W2135484878","doi":"10.1109/icassp.2011.5946882","title":"ECG for blind identity verification in distributed systems","year":2011,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Linear discriminant analysis; Smart card; Matching (statistics); Pattern recognition (psychology); Identity (music); Artificial intelligence; Discriminant; Set (abstract data type); Identification (biology); Autocorrelation; Speech recognition; Feature extraction; Data mining; Computer security; Mathematics; Statistics","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":[0.0001575467,0.0000371995,0.0001069157,0.0000598634,0.00001861584,0.000009052976,0.00003228105,0.00003655875,0.00002283579],"category_scores_gemma":[0.00006109844,0.0000303076,0.00003843116,0.0001545167,0.00000785354,0.00006300456,0.000004701147,0.00003396579,0.0000207795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003328754,"about_ca_system_score_gemma":0.00001245726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007377023,"about_ca_topic_score_gemma":0.00003651612,"domain_scores_codex":[0.9996209,0.00000862255,0.0001300766,0.00009626542,0.00006614429,0.00007803355],"domain_scores_gemma":[0.9997299,0.00001274923,0.00002794136,0.0001330469,0.00006002813,0.00003628475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003464023,0.0005739173,0.9823672,0.0003384997,0.0001587914,0.00001183903,0.0004031659,0.0000389951,0.006406679,0.004930135,0.001705635,0.002718773],"study_design_scores_gemma":[0.006281673,0.0003629156,0.8650185,0.0003084621,0.0005482638,0.0000115083,0.002611228,0.09695985,0.02212976,0.000700366,0.004718431,0.0003490222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8005878,0.000110922,0.1962647,0.0001243426,0.0002344781,0.000288509,0.00001204999,0.00005841369,0.002318763],"genre_scores_gemma":[0.9973121,0.00000992596,0.001264785,0.000007878353,0.00007886244,0.00003059278,0.00005717804,0.000004027007,0.001234603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1967243,"threshold_uncertainty_score":0.1235908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064470937660381,"score_gpt":0.3347577076422983,"score_spread":0.2283106138762603,"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."}}