{"id":"W4376121573","doi":"10.1002/sim.9765","title":"Incorporating biological knowledge in analyses of environmental mixtures and health","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Environmental Health Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Prior probability; Computer science; Bayesian probability; Prior information; Nonparametric statistics; Dirichlet distribution; Set (abstract data type); Index (typography); Data mining; Statistics; Econometrics; Machine learning; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00136484,0.0001035174,0.0004520243,0.0002092983,0.00002441372,0.000002572433,0.00007082551,0.00004773341,0.00009711721],"category_scores_gemma":[0.002105102,0.00007407189,0.00001012268,0.0003395184,0.0003482259,0.00001188871,0.00005994358,0.0001611097,0.000002350315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003451582,"about_ca_system_score_gemma":0.00003258704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001269892,"about_ca_topic_score_gemma":0.0001514294,"domain_scores_codex":[0.998728,0.0002249007,0.0005490094,0.0001782832,0.0001376716,0.0001821242],"domain_scores_gemma":[0.9968705,0.002808676,0.0001417958,0.0001035735,0.00001063097,0.00006483651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002177323,0.0001559347,0.05249745,0.000450237,0.00001893529,0.00008218237,0.001844817,0.000001660928,0.002781179,0.822481,0.002533861,0.1171309],"study_design_scores_gemma":[0.0004663026,0.0002691571,0.1501757,0.0002304158,0.000007586153,0.000002537794,0.0007220612,0.00289607,0.00006755936,0.8450789,0.00001162577,0.00007206282],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1255771,0.001196767,0.8697774,0.0006741342,0.000199491,0.0004603527,0.0006054436,0.00004830473,0.001460995],"genre_scores_gemma":[0.5773196,0.0002781535,0.4222828,0.00004036458,0.00002856312,0.000006586499,0.00002497782,0.000006583301,0.00001240223],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4517425,"threshold_uncertainty_score":0.3020564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2370052430274653,"score_gpt":0.5039231798181436,"score_spread":0.2669179367906783,"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."}}