{"id":"W2964628549","doi":"10.1016/j.gloepi.2019.100005","title":"A matrix for bridging the epidemiology and risk assessment gap","year":2019,"lang":"en","type":"article","venue":"Global Epidemiology","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; University of Ottawa","funders":"American Petroleum Institute; National Institutes of Health; U.S. Environmental Protection Agency","keywords":"Bridging (networking); Multidisciplinary approach; Epidemiology; Risk assessment; Public health; Computer science; Management science; Risk analysis (engineering); Data science; Knowledge management; Medicine; Engineering; Pathology; Sociology; Computer security; Social science","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":[0.01276642,0.0002656176,0.0007119291,0.00001883217,0.0003300568,0.000006497777,0.0003324658,0.0002202461,0.0005960925],"category_scores_gemma":[0.006213952,0.0001914523,0.0001390145,0.0001277014,0.0006094873,0.0001297117,0.0004549673,0.0003935881,0.0007724067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004872965,"about_ca_system_score_gemma":0.00002765831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002267596,"about_ca_topic_score_gemma":0.0001798696,"domain_scores_codex":[0.9940618,0.002912792,0.0007571374,0.001032472,0.0001043856,0.001131423],"domain_scores_gemma":[0.9903671,0.008301283,0.0005042836,0.0005821828,0.000008030739,0.0002371009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002414846,0.00001907465,0.950659,0.00001385883,0.00003102221,0.00000112665,0.00002762455,0.002264944,0.00004067264,0.008053916,0.003875678,0.03498888],"study_design_scores_gemma":[0.0005203278,0.0001709348,0.8731025,0.00001089877,0.00004546714,0.00004650391,0.00007225784,0.02970957,0.000002402688,0.04954699,0.04657097,0.0002011679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.870639,0.0008793651,0.09964217,0.01755363,0.000447122,0.0016484,0.0001221857,0.00006432805,0.009003775],"genre_scores_gemma":[0.9572194,0.001175312,0.02619971,0.01472269,0.0001689592,0.0001867594,0.00002476614,0.00002255859,0.000279799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08658041,"threshold_uncertainty_score":0.9927983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0454041232417613,"score_gpt":0.3817760888421286,"score_spread":0.3363719656003673,"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."}}