{"id":"W2061009972","doi":"10.1503/cmaj.1060143","title":"Training Canada's future clinician-teachers and researchers","year":2006,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Training (meteorology); Action (physics); Medical education; Point (geometry); Computer science; Medicine; Data science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01652008,0.001097962,0.0009090499,0.001499756,0.01965452,0.007644427,0.004198099,0.02329713,0.05815985],"category_scores_gemma":[0.02687937,0.001148872,0.001251882,0.001784381,0.008322896,0.005748456,0.007201946,0.01951538,0.0137991],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03774416,"about_ca_system_score_gemma":0.3227204,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7188891,"about_ca_topic_score_gemma":0.9156563,"domain_scores_codex":[0.9837842,0.003420019,0.0004965269,0.001218086,0.004610178,0.006470971],"domain_scores_gemma":[0.8479981,0.007708499,0.002807649,0.002488017,0.02319187,0.1158058],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002775594,0.00004883044,0.001157893,0.0001356258,0.000007701196,0.0001592495,0.0005943985,0.00002876988,0.0001506981,0.002383431,0.9834369,0.01186868],"study_design_scores_gemma":[0.00005646897,0.00004761522,0.002860048,0.0002386503,0.00001149844,0.0001921074,0.002377194,0.00004311249,0.00006744562,0.001404304,0.9926676,0.00003394689],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0008111365,0.003701926,0.0004503465,0.9682031,0.01451575,0.00008679361,0.0001873066,0.0001463021,0.01189736],"genre_scores_gemma":[0.0144342,0.006493893,0.004471951,0.8344911,0.004846001,0.0003267448,0.0003535056,0.0001019793,0.1344805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9834799,"threshold_uncertainty_score":0.5655329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09687700613669513,"score_gpt":0.3976815158146891,"score_spread":0.300804509677994,"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."}}