{"id":"W2023618638","doi":"10.1109/icassp.2013.6638210","title":"Design of a Hamming-distance classifier for ECG biometrics","year":2013,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Hamming distance; Classifier (UML); Computer science; Euclidean distance; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Hamming code; Decoding methods; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001188461,0.0004777204,0.001095322,0.0009953571,0.0005709858,0.001111292,0.001613484,0.0011357,0.002172788],"category_scores_gemma":[0.003049683,0.0003903336,0.0003998549,0.0006324493,0.0003323133,0.001523357,0.0006831068,0.0006869556,0.002105169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000695523,"about_ca_system_score_gemma":0.001024223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001587139,"about_ca_topic_score_gemma":0.001316637,"domain_scores_codex":[0.9984262,0.0002179767,0.0001567985,0.0004225262,0.0006502266,0.0001264056],"domain_scores_gemma":[0.9982475,0.0003118864,0.0001180795,0.0001508311,0.001079811,0.00009195355],"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":[0.0009557213,0.0003639668,0.003421259,0.0002512161,0.0001425315,0.0002604497,0.0001294575,0.03086269,0.2193192,0.007462448,0.004801597,0.7320294],"study_design_scores_gemma":[0.00006533389,0.0006265776,0.00292443,0.000023849,0.00005875831,0.00085577,0.00004611136,0.8750058,0.111225,0.001542711,0.007569782,0.0000558113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01499549,0.0002677024,0.9826394,0.0001019085,0.000120634,0.0001689354,0.00007269143,0.0009589107,0.0006741766],"genre_scores_gemma":[0.3447963,0.0003515054,0.649826,0.0002105866,0.0001862861,0.0003652181,0.0004381926,0.00008382899,0.003742021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002172788,"threshold_uncertainty_score":0.007268727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06697658699834552,"score_gpt":0.3057807731281464,"score_spread":0.2388041861298009,"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."}}