{"id":"W2336817058","doi":"10.1503/cmaj.151209","title":"Naming and classifying old and new ECG phenomena","year":2016,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Data science; World Wide Web; Speech recognition; Information retrieval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004557025,0.0009163778,0.0009255061,0.00403156,0.001147894,0.002481291,0.001644097,0.004154878,0.005894815],"category_scores_gemma":[0.02469776,0.0002492223,0.0004200671,0.001307092,0.003904531,0.006250173,0.001815486,0.007083592,0.008205408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000976263,"about_ca_system_score_gemma":0.001466744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134098,"about_ca_topic_score_gemma":0.002528215,"domain_scores_codex":[0.996554,0.001427859,0.0008296925,0.0002933824,0.000653676,0.0002413956],"domain_scores_gemma":[0.9936899,0.002564201,0.001092183,0.0005701126,0.001543358,0.0005402471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000248855,0.0001202808,0.02188032,0.0007972131,0.00004446024,0.03349676,0.002990971,0.0003177026,0.006357932,0.06051058,0.4998539,0.373381],"study_design_scores_gemma":[0.0001341503,0.0002348865,0.01506932,0.00299635,0.00006782566,0.2716516,0.004899822,0.002641944,0.003582987,0.1180813,0.5804537,0.0001861164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.03587704,0.0696011,0.1528402,0.5138017,0.07779754,0.00128667,0.001673024,0.001833221,0.1452895],"genre_scores_gemma":[0.289364,0.06103761,0.19369,0.2663167,0.1484827,0.001052646,0.002240442,0.0009304808,0.03688539],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.005894815,"threshold_uncertainty_score":0.02410018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009489662195327866,"score_gpt":0.2261199318409646,"score_spread":0.2166302696456367,"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."}}