{"id":"W2135484878","doi":"10.1109/icassp.2011.5946882","title":"ECG for blind identity verification in distributed systems","year":2011,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Linear discriminant analysis; Smart card; Matching (statistics); Pattern recognition (psychology); Identity (music); Artificial intelligence; Discriminant; Set (abstract data type); Identification (biology); Autocorrelation; Speech recognition; Feature extraction; Data mining; Computer security; Mathematics; Statistics","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":[0.002860217,0.0006312224,0.0008855041,0.0007882415,0.00081461,0.001734513,0.0009717168,0.00181618,0.007943087],"category_scores_gemma":[0.0106284,0.0002427598,0.0003311163,0.0007474013,0.001222782,0.002757688,0.001649437,0.00114706,0.002471399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006312507,"about_ca_system_score_gemma":0.0009062138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000727184,"about_ca_topic_score_gemma":0.0006199471,"domain_scores_codex":[0.9965114,0.001518464,0.0001756102,0.0005597947,0.001019897,0.0002149365],"domain_scores_gemma":[0.9948906,0.002196552,0.0003118159,0.00172996,0.0007715449,0.00009955756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001072265,0.0001787057,0.002492673,0.0005565453,0.00008924031,0.0007533071,0.0002966817,0.0630543,0.02903608,0.2409402,0.01705574,0.6444743],"study_design_scores_gemma":[0.0001755592,0.00046802,0.002682663,0.0002606385,0.00007863506,0.001519458,0.0002900163,0.6366757,0.04666958,0.2387875,0.07228974,0.0001024384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009174258,0.002494525,0.9767867,0.0007529479,0.000472092,0.0001170258,0.0001352261,0.001256308,0.008811008],"genre_scores_gemma":[0.5973961,0.002199435,0.3876358,0.0005910026,0.0006612697,0.0002040744,0.0002907091,0.0001959709,0.01082553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007943087,"threshold_uncertainty_score":0.02657229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064470937660381,"score_gpt":0.3347577076422983,"score_spread":0.2283106138762603,"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."}}