{"id":"W2766454159","doi":"10.1177/1541931213601903","title":"Improving Machinery-Related Risk Identification and Estimation with Accident Reporting and Logical Analysis of Data","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Risk analysis (engineering); Harm; Identification (biology); Estimation; Accident (philosophy); Computer science; Risk assessment; Cornerstone; Plan (archaeology); Action (physics); Hierarchy; Action plan; Actuarial science; Engineering; Computer security; Business; Psychology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01636524,0.001739752,0.001695838,0.008601898,0.0008151248,0.005077404,0.003169201,0.0009533496,0.00134772],"category_scores_gemma":[0.08681577,0.001241925,0.001883167,0.006448906,0.0008129656,0.007720375,0.003009495,0.001585108,0.0009607688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117606,"about_ca_system_score_gemma":0.00354296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005353869,"about_ca_topic_score_gemma":0.00528301,"domain_scores_codex":[0.982131,0.007500722,0.002757799,0.002082215,0.005232756,0.0002954839],"domain_scores_gemma":[0.909264,0.05420688,0.01308359,0.01257993,0.01047516,0.0003904932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003269653,0.00105885,0.1087144,0.00125962,0.0005055411,0.0002760739,0.001580381,0.07574531,0.01467734,0.009522723,0.003573488,0.7827594],"study_design_scores_gemma":[0.0001303887,0.0006968022,0.0360954,0.0004825149,0.0004539333,0.0007147261,0.001826309,0.8819343,0.03126088,0.03151277,0.01456704,0.0003249148],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01848876,0.0001705013,0.9772879,0.0004093033,0.00002240265,0.0004399439,0.0006786293,0.001875709,0.0006268062],"genre_scores_gemma":[0.1158349,0.0002488498,0.8811716,0.0000815011,0.00004349769,0.0004259757,0.001824499,0.0001032642,0.0002658868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01636524,"threshold_uncertainty_score":0.08654875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0979773274651616,"score_gpt":0.4016513514474327,"score_spread":0.3036740239822711,"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."}}