{"id":"W2088544562","doi":"10.1109/icassp.2010.5495461","title":"Signal validation for cardiac biometrics","year":2010,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Liveness; Biometrics; Computer science; Fingerprint (computing); Feature extraction; Artificial intelligence; Pattern recognition (psychology); SIGNAL (programming language); Spoofing attack; Feature (linguistics); Fingerprint recognition; Face (sociological concept); Iris recognition; Matching (statistics); Speech recognition; Computer security; 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":[0.0001598108,0.00003499641,0.00009253596,0.0001766176,0.00003154403,0.00001091187,0.00001950276,0.00004254297,0.000101433],"category_scores_gemma":[0.000111866,0.00002652557,0.00009428336,0.0003446315,0.000008644741,0.00002037517,0.000004518184,0.00005574716,0.00002600766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006437084,"about_ca_system_score_gemma":0.00001568567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002254704,"about_ca_topic_score_gemma":4.13973e-7,"domain_scores_codex":[0.999682,0.000003081452,0.00006847993,0.00007992928,0.00009236413,0.00007410922],"domain_scores_gemma":[0.9996758,0.00006328579,0.00001557058,0.00009842434,0.00009325604,0.00005369514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002005575,0.00009129378,0.1588536,0.0000436156,0.0001477115,0.000001033395,0.00003165097,0.00000221929,0.7066212,0.0005724849,0.006838847,0.1267764],"study_design_scores_gemma":[0.0006890777,0.0001923726,0.01352181,0.000009031251,0.0004319887,0.000002164935,0.00008941239,0.002085106,0.8747276,0.0002036021,0.1079146,0.0001332785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719803,0.00003190003,0.01970821,0.0006948029,0.0006348631,0.0001383708,0.000006200096,0.00008168847,0.006723702],"genre_scores_gemma":[0.9768428,0.000005400013,0.01581005,0.00002695769,0.0006982785,0.000008793451,0.00002993261,0.000005906232,0.006571856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1681065,"threshold_uncertainty_score":0.111062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02309178062510034,"score_gpt":0.3176879181241524,"score_spread":0.2945961374990521,"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."}}