{"id":"W2535940264","doi":"10.1109/bcc.2006.4341628","title":"ECG Biometric Recognition Without Fiducial Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":245,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Fiducial marker; Computer science; Artificial intelligence; Heartbeat; Discrete cosine transform; Pattern recognition (psychology); Waveform; Autocorrelation; False positive rate; Computer vision; Mathematics; Computer security; 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":[0.0000861296,0.00005566052,0.0001039763,0.0004333832,0.00005322767,0.00001289034,0.00001425229,0.00005066212,0.0001757929],"category_scores_gemma":[0.00004589991,0.00004510331,0.00006670361,0.0008892138,0.00001142389,0.00003755808,0.000004561051,0.00006051399,0.0002158176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000394897,"about_ca_system_score_gemma":0.000009807844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008254882,"about_ca_topic_score_gemma":0.00005881518,"domain_scores_codex":[0.999521,0.00001099251,0.0001133562,0.0001250747,0.0001297413,0.00009983915],"domain_scores_gemma":[0.9997669,0.0000138509,0.00002842575,0.0000856366,0.00006992915,0.00003518535],"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":[0.00006209336,0.0001993906,0.1426858,0.00002998823,0.0000556299,0.00001021735,0.00001065522,0.000003687277,0.09779742,0.000002291811,0.001092721,0.7580501],"study_design_scores_gemma":[0.002170085,0.0004943803,0.242744,0.0000787157,0.0006585011,0.00007552065,0.0001283173,0.0066005,0.7388344,0.0005979474,0.007281417,0.0003362127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701369,0.00005273774,0.01045837,0.0001553196,0.0001955288,0.0000573779,0.000001042833,0.0001463882,0.01879633],"genre_scores_gemma":[0.9887387,0.000009373939,0.002705725,0.00004577259,0.0009449639,0.000004862009,0.00002287844,0.00000778788,0.007519911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7577139,"threshold_uncertainty_score":0.277397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950612856830704,"score_gpt":0.2675946561948083,"score_spread":0.2480885276265012,"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."}}