{"id":"W3119638420","doi":"10.1111/biom.13569","title":"Bayesian multiple index models for environmental mixtures","year":2021,"lang":"en","type":"preprint","venue":"Biometrics","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Environmental Health Sciences; Environmental Protection Agency","keywords":"Interpretability; Linear model; Index (typography); Bayesian probability; Curse of dimensionality; Additive model; Econometrics; Range (aeronautics); Statistics; Bayesian inference; Computer science; Flexibility (engineering); Mathematics; Data mining; Artificial intelligence","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":[0.0004412875,0.0003250696,0.0003294982,0.0003113861,0.0001965671,0.0001427495,0.0004668595,0.0004783946,0.0001874098],"category_scores_gemma":[0.0002790943,0.0003405325,0.0003309831,0.0006963307,0.0001417301,0.0001269857,0.001476568,0.0003777296,0.00002493145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005074766,"about_ca_system_score_gemma":0.00002113276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000244352,"about_ca_topic_score_gemma":0.000008228702,"domain_scores_codex":[0.9977452,0.00005768954,0.0003831996,0.0007827315,0.0005865642,0.000444641],"domain_scores_gemma":[0.9987627,0.0002819498,0.0002277107,0.0005366682,0.000007422172,0.0001836042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007261794,0.0009277909,0.2330813,0.0004228731,0.0002430444,0.00003463731,0.001815957,0.3066946,0.005910744,0.00001299061,0.00469683,0.4460866],"study_design_scores_gemma":[0.0007948463,0.0001106948,0.02008783,0.0001003613,0.0001031687,0.000008524594,0.0004969173,0.9594656,0.004393563,0.001415684,0.01178934,0.00123342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3497373,0.00144833,0.6432091,0.0001051663,0.002153796,0.0008619726,0.0004700865,0.0001535561,0.001860722],"genre_scores_gemma":[0.9704787,0.00009654371,0.02770997,0.00006212467,0.0003111166,0.00009121626,0.0002986471,0.00005176946,0.0008999729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6527711,"threshold_uncertainty_score":0.9999047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05136045198016322,"score_gpt":0.2680955432914371,"score_spread":0.2167350913112739,"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."}}