{"id":"W4243433825","doi":"10.1515/iupac.79.2239","title":"Rate in Epidemiology","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Terminology; Glossary; Chemical nomenclature; Relation (database); Meaning (existential); Computer science; Biostatistics; Management science; Epistemology; Chemistry; Medicine; Epidemiology; Linguistics; Engineering; Pathology; Philosophy; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003329932,0.001436587,0.001784199,0.005606588,0.0006852575,0.003517928,0.002901495,0.001861333,0.07042854],"category_scores_gemma":[0.03030491,0.0006482187,0.002510316,0.009151095,0.0004271269,0.002216897,0.002303243,0.002665864,0.05170508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001738254,"about_ca_system_score_gemma":0.003053825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01633962,"about_ca_topic_score_gemma":0.02421472,"domain_scores_codex":[0.9959943,0.001179899,0.00071271,0.001126176,0.0006689891,0.0003179009],"domain_scores_gemma":[0.9884415,0.004880484,0.001912381,0.002540971,0.001700831,0.0005239442],"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":[0.0001852599,0.00003416159,0.01416908,0.002511913,0.0002356593,0.00005422805,0.00005257667,0.001102298,0.000064472,0.003354118,0.9622182,0.01601798],"study_design_scores_gemma":[0.0003076984,0.00003336652,0.01381438,0.001034986,0.0001862089,0.0003672146,0.0001013756,0.001198245,0.0001751889,0.006973126,0.9757633,0.00004479776],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005354094,0.001299156,0.0006464627,0.0005819814,0.0001480773,0.00003658143,0.9941099,0.0003309445,0.002311596],"genre_scores_gemma":[0.004791371,0.001322935,0.001974668,0.0005336769,0.0001302594,0.0002650892,0.9885052,0.0001674051,0.002309403],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07042854,"threshold_uncertainty_score":0.235607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1529037961759136,"score_gpt":0.5649988680680189,"score_spread":0.4120950718921053,"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."}}