{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001254161,0.00004157905,0.0001088145,0.0002702841,0.00002976294,0.000009772374,0.0000150922,0.00004602373,0.0004976217],"category_scores_gemma":[0.0001076282,0.00003167,0.00008692718,0.0006584893,0.00001398741,0.00002537973,0.000006490945,0.0001230739,0.0006260371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006068707,"about_ca_system_score_gemma":0.00001236191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001196089,"about_ca_topic_score_gemma":0.000003020532,"domain_scores_codex":[0.9996223,0.000006575569,0.00007525247,0.00009702804,0.0001062425,0.0000926331],"domain_scores_gemma":[0.9996766,0.00002547743,0.0000123131,0.0001379566,0.00006102807,0.0000865966],"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":[0.00001071852,0.0001008189,0.2099061,0.00001905062,0.00009754221,0.000007376804,0.00002318311,5.4998e-8,0.313601,0.00001738479,0.00501242,0.4712044],"study_design_scores_gemma":[0.001172963,0.0003530199,0.3402249,0.0000503753,0.0007283376,0.00004138084,0.0002745549,0.000496176,0.5129973,0.0003603632,0.1429122,0.0003884707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748349,0.00004320525,0.00008519396,0.0006239401,0.0004542285,0.0000391015,0.000001816592,0.00008804581,0.02382962],"genre_scores_gemma":[0.9890274,0.00002523746,0.004654329,0.00008765979,0.0005787345,0.000003783367,0.00001749537,0.00000621721,0.005599174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4708159,"threshold_uncertainty_score":0.8046649,"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."}}