{"id":"W2754375805","doi":"10.1093/occmed/kqx136","title":"Developing a tool for identifying high-risk employers for inspection","year":2017,"lang":"en","type":"article","venue":"Occupational Medicine","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Alberta","funders":"WorkSafeBC","keywords":"Index (typography); Proxy (statistics); Confidence interval; Medicine; Receiver operating characteristic; Enforcement; Statistics; Actuarial science; Operations management; Business; Engineering; Computer science; Internal medicine; Mathematics; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02523465,0.001247218,0.001273783,0.01502918,0.0007459281,0.004174488,0.002078595,0.001504374,0.004134763],"category_scores_gemma":[0.09541889,0.0005794704,0.001858062,0.006577419,0.0004809705,0.003161179,0.00228372,0.001097917,0.00205681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001949739,"about_ca_system_score_gemma":0.004296545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009622176,"about_ca_topic_score_gemma":0.01183494,"domain_scores_codex":[0.9892666,0.003388141,0.001970334,0.0009560568,0.003818227,0.0006005617],"domain_scores_gemma":[0.9269779,0.04432321,0.01066445,0.00217872,0.01377843,0.002077245],"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":[0.0004880394,0.000489015,0.7220406,0.0008921015,0.0003399149,0.000169376,0.000971538,0.001980364,0.0006608364,0.001177216,0.01930945,0.2514814],"study_design_scores_gemma":[0.0004006351,0.00170372,0.8601674,0.00206035,0.0009039256,0.001251025,0.004436329,0.08454783,0.004633592,0.007421487,0.03215913,0.0003146527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6967543,0.004456939,0.2162845,0.005035578,0.0007417281,0.00764435,0.02707879,0.00946584,0.03253797],"genre_scores_gemma":[0.6863347,0.0009223041,0.2953872,0.0006732599,0.0001420572,0.003544641,0.010456,0.0001287862,0.002411178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02523465,"threshold_uncertainty_score":0.1334552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3014472806685438,"score_gpt":0.5711953702507243,"score_spread":0.2697480895821805,"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."}}