{"id":"W4281492300","doi":"10.1111/biom.13702","title":"Semiparametric Distributed Lag Quantile Regression for Modeling Time-Dependent Exposure Mixtures","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; National Institutes of Health; York University","keywords":"Quantile regression; Lag; Distributed lag; Econometrics; Quantile; Semiparametric model; Statistics; Semiparametric regression; Regression; Regression analysis; Time lag; Computer science; Mathematics; Nonparametric statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001316,0.0001571029,0.0002153645,0.0005448394,0.0007189531,0.00004129455,0.0003713906,0.0001015285,0.001294732],"category_scores_gemma":[0.0007224196,0.0001414479,0.00008929873,0.004965159,0.00003906371,0.0001318679,0.0004042419,0.0002134,0.0001458872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005334061,"about_ca_system_score_gemma":0.00003554731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001891118,"about_ca_topic_score_gemma":0.0000038363,"domain_scores_codex":[0.9978322,0.0001311751,0.000351842,0.000380919,0.0008121107,0.000491786],"domain_scores_gemma":[0.9989383,0.0003347758,0.00016714,0.0003079708,0.00001863735,0.000233205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000832769,0.002301549,0.02896195,0.0003437635,0.00007456908,0.00004106712,0.001678013,0.313317,0.01284067,0.0003090194,0.4957622,0.1435374],"study_design_scores_gemma":[0.001872868,0.001456675,0.004055889,0.00002039729,0.00005626842,0.00002510402,0.0004637376,0.7474371,0.002128981,0.001310063,0.2403902,0.0007827341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8509543,0.002304579,0.1394385,0.002334939,0.0008748602,0.001394817,0.001699778,0.000273457,0.0007248088],"genre_scores_gemma":[0.9929214,0.00005612569,0.00452997,0.0009126578,0.00006578969,0.00008927937,0.0002898782,0.00002674587,0.001108139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4341201,"threshold_uncertainty_score":0.9996182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06087264841277221,"score_gpt":0.3202755744316261,"score_spread":0.2594029260188538,"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."}}