{"id":"W2002422013","doi":"10.1155/2008/148658","title":"Analysis of Human Electrocardiogram for Biometric Recognition","year":2007,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":339,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Centres of Excellence","keywords":"Fiducial marker; Biometrics; Computer science; Discrete cosine transform; Artificial intelligence; Pattern recognition (psychology); Heartbeat; Identification (biology); Autocorrelation; Computer vision; Speech recognition; Image (mathematics); Mathematics","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":[0.001502948,0.000143654,0.000563971,0.004257767,0.0001654994,0.00003666793,0.00008965448,0.00006644457,0.00001827529],"category_scores_gemma":[0.0001763851,0.0001200585,0.0004119508,0.006124343,0.00004878566,0.0002626716,0.000006247104,0.0003451946,0.000001049114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000134108,"about_ca_system_score_gemma":0.0000426877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001920622,"about_ca_topic_score_gemma":0.000004487039,"domain_scores_codex":[0.9983087,0.00003711023,0.0006750636,0.0002162499,0.0004357884,0.0003270822],"domain_scores_gemma":[0.9987293,0.0002186654,0.0004733634,0.00008537242,0.0003686118,0.0001246852],"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":[0.000392275,0.0002456486,0.09598711,0.0001198041,0.0003966151,0.00004147084,0.00007515302,0.000606301,0.02070019,0.000003359334,0.000004023278,0.8814281],"study_design_scores_gemma":[0.01832647,0.01893893,0.3917195,0.008654032,0.02636319,0.0006810559,0.005050173,0.03759329,0.4734502,0.008895256,0.007709622,0.00261829],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8370414,0.008126182,0.152945,0.00005455655,0.00008699782,0.0001393126,0.000003596594,0.00002984867,0.00157311],"genre_scores_gemma":[0.9937466,0.0003863498,0.005325578,0.00005893605,0.0003979785,0.000003519133,0.00001825463,0.0000178321,0.0000449322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8788098,"threshold_uncertainty_score":0.4895843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03485848196319918,"score_gpt":0.3841625775846549,"score_spread":0.3493040956214558,"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."}}