{"id":"W2161642170","doi":"10.1080/15459624.2015.1053892","title":"Using checklists and algorithms to improve qualitative exposure judgment accuracy","year":2015,"lang":"en","type":"article","venue":"Journal of Occupational and Environmental Hygiene","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","funders":"National Institute for Occupational Safety and Health","keywords":"Checklist; Applied psychology; Clinical judgment; Qualitative property; Psychology; Documentation; Heuristics; Qualitative research; Exposure assessment; Set (abstract data type); Medical education; Medicine; Computer science; Medical physics; Machine learning; Cognitive psychology; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001685421,0.0001311626,0.0002568933,0.0001440766,0.0003393907,0.00001188858,0.00008864798,0.00009094634,0.0000930731],"category_scores_gemma":[0.0004191414,0.0001060377,0.00003903981,0.00009294202,0.00008809807,0.0003300613,0.0001477932,0.0003541632,0.00002540569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003845397,"about_ca_system_score_gemma":0.0003566071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008229756,"about_ca_topic_score_gemma":0.000006723874,"domain_scores_codex":[0.9977942,0.0003458815,0.0006780236,0.0001789115,0.0006982348,0.0003048098],"domain_scores_gemma":[0.9980791,0.0006633417,0.0003522087,0.0000901564,0.0001273329,0.0006878873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01155815,0.0009544098,0.7660053,0.0005265529,0.0002270959,0.00007477628,0.06667649,0.0008262923,0.01415017,0.001265634,0.004435452,0.1332996],"study_design_scores_gemma":[0.003722829,0.001573913,0.9605029,0.0001673459,0.00002946502,0.0000679813,0.02320926,0.001502014,0.000195397,0.001238047,0.007539333,0.0002515369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957764,0.0005970467,0.001461684,0.0009941846,0.0003745795,0.0004168724,0.0001646009,0.000003879859,0.0002107806],"genre_scores_gemma":[0.988437,0.0001499051,0.0100627,0.0006080144,0.0004335274,0.00002062501,0.00002893025,0.00001256953,0.0002467547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1944975,"threshold_uncertainty_score":0.4324091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2189750593756596,"score_gpt":0.5090856635971803,"score_spread":0.2901106042215207,"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."}}