{"id":"W2147380014","doi":"10.1109/ntms.2008.ecp.29","title":"Biometric Identification System Based on Electrocardiogram Data","year":2008,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Biometrics; Computer science; Identification (biology); Set (abstract data type); Signal processing; Artificial intelligence; SIGNAL (programming language); Variety (cybernetics); Data mining; Data set; Digital signal processing; Pattern recognition (psychology); Machine learning; Computer hardware","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":[0.0002352077,0.00006588026,0.0001584981,0.0006339782,0.00008291829,0.00001225121,0.000126097,0.00003773395,0.00001278545],"category_scores_gemma":[0.00009039393,0.00005061493,0.00006934145,0.001730914,0.00001479736,0.00003823469,0.00001468353,0.00006630261,0.0001977279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005911446,"about_ca_system_score_gemma":0.00003561595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007438615,"about_ca_topic_score_gemma":4.423092e-7,"domain_scores_codex":[0.9991497,0.00002347981,0.0001555478,0.0002519031,0.0002986556,0.0001206693],"domain_scores_gemma":[0.9988775,0.00004056579,0.00003866272,0.0009118311,0.00006144874,0.00006999533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003097547,0.00170419,0.7282343,0.0008098379,0.001345409,0.0005389501,0.00006090335,0.0003768013,0.06222429,0.0003673438,0.07294653,0.1310817],"study_design_scores_gemma":[0.002385882,0.0006542627,0.2830075,0.000248452,0.001154514,0.0002047322,0.0002368052,0.6425626,0.05523716,0.000003041776,0.01385874,0.0004462892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7039775,0.0009981313,0.2114099,0.001759157,0.0009529567,0.0007393391,0.00004116807,0.001881226,0.07824068],"genre_scores_gemma":[0.9956253,0.00002650562,0.001195262,0.00005545778,0.0002292274,0.000005274607,0.0002099568,0.000009326077,0.00264371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6421858,"threshold_uncertainty_score":0.2541458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06974687530822352,"score_gpt":0.3108837474877183,"score_spread":0.2411368721794948,"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."}}