{"id":"W2093451445","doi":"10.1109/btas.2010.5634493","title":"HeartID: Cardiac biometric recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Discriminative model; Computer science; Feature extraction; Pattern recognition (psychology); Artificial intelligence; Wavelet; Dependency (UML); Population; Speech recognition; Noise (video); Modal; Feature (linguistics); Medicine; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098297,0.001046239,0.0007799835,0.001978915,0.0004162556,0.001366744,0.0009809177,0.0012443,0.03114561],"category_scores_gemma":[0.001766957,0.0001832654,0.0002799199,0.001144521,0.0003067261,0.001202284,0.001759851,0.000847886,0.03509201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666801,"about_ca_system_score_gemma":0.0003341789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004266101,"about_ca_topic_score_gemma":0.0002820911,"domain_scores_codex":[0.9988145,0.000220977,0.0001097791,0.0002926099,0.0004573945,0.0001048667],"domain_scores_gemma":[0.9993691,0.0001172877,0.00008338851,0.0001427655,0.0001992532,0.00008818794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003808695,0.0001250177,0.002145123,0.0004915144,0.00003747457,0.0003620983,0.00007042696,0.0006247599,0.03667101,0.01548639,0.1229547,0.8206506],"study_design_scores_gemma":[0.0001155478,0.0005780228,0.0158287,0.0003329397,0.00008862973,0.005864345,0.000110249,0.03307576,0.06031491,0.01491129,0.8686396,0.0001399286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01974259,0.01397414,0.7739781,0.003514753,0.00539804,0.0009870307,0.01354345,0.0550482,0.1138137],"genre_scores_gemma":[0.2670572,0.008434623,0.4252069,0.006651088,0.005070551,0.001829643,0.04995361,0.001959874,0.2338365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03114561,"threshold_uncertainty_score":0.1041924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149253940789676,"score_gpt":0.2884205483976011,"score_spread":0.2669280089897044,"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."}}