{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001430322,0.0007960803,0.001035511,0.001700324,0.0002584,0.0008515165,0.000566415,0.0008966026,0.002037199],"category_scores_gemma":[0.003446464,0.0002116497,0.0006053803,0.001303563,0.0002729126,0.001008209,0.001049303,0.0006328639,0.001637205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964951,"about_ca_system_score_gemma":0.0002492943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003790384,"about_ca_topic_score_gemma":0.0004312387,"domain_scores_codex":[0.9989555,0.0002869805,0.00005240262,0.0002081438,0.0004221189,0.00007484712],"domain_scores_gemma":[0.9991133,0.0003327929,0.00006498167,0.0001991667,0.0002624562,0.00002721952],"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":[0.0007220771,0.0001474309,0.002657275,0.0003391152,0.0001690992,0.0003238576,0.000174263,0.02510221,0.06334069,0.004370963,0.003064159,0.8995888],"study_design_scores_gemma":[0.00009833874,0.0008058551,0.01983851,0.0002383967,0.0004023222,0.002284407,0.0002674458,0.7706693,0.1569509,0.02740695,0.02086106,0.000176485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0365483,0.003674049,0.9522538,0.0003704135,0.0002837649,0.0000933132,0.0003543059,0.001499041,0.004923119],"genre_scores_gemma":[0.6975719,0.003059556,0.2944134,0.000233844,0.0003481065,0.0000614023,0.0008252947,0.00007808306,0.003408433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002037199,"threshold_uncertainty_score":0.007564306,"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."}}