{"id":"W2099980260","doi":"10.1109/isccsp.2008.4537472","title":"Fusion of ECG sources for human identification","year":2008,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Computer science; Identification (biology); Artificial intelligence; Pattern recognition (psychology); Sensor fusion; Population; Feature (linguistics); Information fusion; Feature extraction; Fiducial marker; Data mining; Machine learning","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":[0.00006551004,0.00002628311,0.00008502079,0.00005120909,0.00006718073,0.000001541936,0.0000204661,0.0000199513,0.00004050275],"category_scores_gemma":[0.00002338197,0.00002010243,0.00006122745,0.00005955952,0.00001894568,0.00001452892,0.000004453557,0.0000164062,0.000005912426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005510075,"about_ca_system_score_gemma":0.000006169326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006900567,"about_ca_topic_score_gemma":0.000003243725,"domain_scores_codex":[0.9996964,0.00000348711,0.0001171455,0.00006516383,0.00007522525,0.00004261862],"domain_scores_gemma":[0.9997565,0.00001350583,0.00003945196,0.0001022663,0.00006747682,0.0000207742],"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":[0.00001495652,0.000105114,0.2010134,0.00006409753,0.00003872304,9.341689e-7,0.0002864013,0.000005304181,0.792353,0.0001249619,0.001229781,0.004763319],"study_design_scores_gemma":[0.0007022962,0.000161118,0.2020078,0.00004450937,0.0001584871,0.000006781859,0.000388306,0.001281649,0.7929699,0.0002202077,0.001992911,0.00006600513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993868,0.00003977153,0.00479978,0.0001508329,0.00002708828,0.00005455043,5.96578e-7,0.0000225667,0.001036845],"genre_scores_gemma":[0.9821735,0.00001763791,0.001420104,0.00001271194,0.0001043073,0.000004241067,0.00001362074,0.000003579978,0.01625032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01521347,"threshold_uncertainty_score":0.08197535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04765227369639126,"score_gpt":0.3291632400637957,"score_spread":0.2815109663674045,"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."}}