{"id":"W4414196550","doi":"10.1002/sim.70258","title":"Collapsible Kernel Machine Regression for Exposomic Analyses","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"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; National Institutes of Health","keywords":"Additive model; Kernel (algebra); Causal inference; Bayesian probability; Flexibility (engineering); Covariate; Kernel method; Inference; Bayesian inference; Regression","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":[0.01540844,0.001448562,0.002280859,0.002203025,0.0008424107,0.00170676,0.002928076,0.001616125,0.003714781],"category_scores_gemma":[0.05271569,0.0009475386,0.00217952,0.003577417,0.001699916,0.002165222,0.004159055,0.003295218,0.001358855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054824,"about_ca_system_score_gemma":0.002174041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004503349,"about_ca_topic_score_gemma":0.003750887,"domain_scores_codex":[0.9900188,0.00644094,0.0004080612,0.001542517,0.001287678,0.0003018541],"domain_scores_gemma":[0.9688078,0.02125262,0.002258161,0.0058389,0.001498172,0.0003443311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003785227,0.0002608218,0.009579847,0.0004681425,0.0008244067,0.000599868,0.0004081549,0.4626169,0.003557773,0.2465125,0.005461334,0.2693317],"study_design_scores_gemma":[0.00002437369,0.00005565351,0.001042287,0.0000297875,0.00003785765,0.00006755392,0.00002648183,0.858668,0.0006182957,0.1365666,0.002828521,0.00003445105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003094863,0.0001596788,0.9960356,0.00009113518,0.0000157151,0.00003319549,0.00008980855,0.000280699,0.0001993379],"genre_scores_gemma":[0.1929936,0.0005361098,0.8000407,0.0002902687,0.0001799113,0.0007564442,0.001070221,0.0004577616,0.003674983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01540844,"threshold_uncertainty_score":0.08148861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04023429796767516,"score_gpt":0.4032869001057561,"score_spread":0.3630526021380809,"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."}}