{"id":"W2055418247","doi":"10.1289/ehp.1308062","title":"Evaluating Uncertainty to Strengthen Epidemiologic Data for Use in Human Health Risk Assessments","year":2014,"lang":"en","type":"article","venue":"Environmental Health Perspectives","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Bayer CropScience; Drexel University; DuPont; Dow Chemical Company","keywords":"Risk assessment; Risk analysis (engineering); Data science; Environmental epidemiology; Exposure assessment; Computer science; Uncertainty; Management science; Environmental health; Medicine; Engineering; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01111357,0.0004992537,0.0007747514,0.0001509105,0.001098816,0.0000498947,0.0008700102,0.0001285426,0.0007155018],"category_scores_gemma":[0.001214007,0.0005079161,0.00009521323,0.0002444647,0.0003607823,0.0006986907,0.001099339,0.0006632401,0.0002477757],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005240924,"about_ca_system_score_gemma":0.00007542359,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060522,"about_ca_topic_score_gemma":0.003969921,"domain_scores_codex":[0.9910533,0.003031064,0.00113414,0.002510734,0.0007098373,0.001560932],"domain_scores_gemma":[0.9953627,0.001446245,0.0007158283,0.001689673,0.000003574923,0.0007819823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000195714,0.002097228,0.6023722,0.0001123852,0.00005450342,0.000003046584,0.01137566,0.0495885,0.001037049,0.0002759205,0.00275601,0.3301318],"study_design_scores_gemma":[0.002001641,0.003071334,0.8911703,0.0001205016,0.00001557071,0.000003663029,0.008468581,0.08277442,0.00001533202,0.0010054,0.01068181,0.0006714701],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761794,0.0002202459,0.01588867,0.002689942,0.0001112127,0.003689418,0.0008471548,0.0000797671,0.0002942362],"genre_scores_gemma":[0.9374396,0.0004362663,0.05655161,0.004426723,0.0001347575,0.0003192734,0.0004574825,0.00007886242,0.0001553771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3294603,"threshold_uncertainty_score":0.9997373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2093061821813448,"score_gpt":0.4706219104984345,"score_spread":0.2613157283170898,"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."}}