{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01703657,0.0011004,0.00118674,0.003432459,0.0006874491,0.002088431,0.001809987,0.001821284,0.001815231],"category_scores_gemma":[0.06124986,0.001023371,0.001562914,0.002291021,0.004595105,0.003775029,0.003728049,0.0028229,0.0003845215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601471,"about_ca_system_score_gemma":0.00181922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00314551,"about_ca_topic_score_gemma":0.003222032,"domain_scores_codex":[0.9902508,0.006988132,0.0002442158,0.001010586,0.001333323,0.0001728407],"domain_scores_gemma":[0.9551125,0.03925469,0.002386176,0.002352116,0.0006827872,0.0002118031],"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":[0.0001161546,0.0001147106,0.006338337,0.0003272639,0.0002723093,0.0001385051,0.0003122684,0.3399983,0.002260907,0.5270074,0.0009128872,0.1222009],"study_design_scores_gemma":[0.00001809821,0.00005317746,0.001542778,0.00005059018,0.00003831361,0.00005060871,0.00004020417,0.3170395,0.001062987,0.6773745,0.002696709,0.0000323787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00509062,0.0003982256,0.9931508,0.0003824439,0.00001527744,0.00002486746,0.00007677863,0.00007878557,0.0007820963],"genre_scores_gemma":[0.303145,0.00184266,0.6912934,0.0005084947,0.0001814881,0.0003938215,0.0004098293,0.0001093581,0.002115913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01703657,"threshold_uncertainty_score":0.0900991,"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."}}