{"id":"W2290828331","doi":"10.1007/s11869-016-0398-z","title":"A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollution","year":2016,"lang":"en","type":"article","venue":"Air Quality Atmosphere & Health","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health; Public Health Ontario; University of Ottawa; Health Canada","funders":"Government of Canada","keywords":"Environmental health; Exposure assessment; Population; Statistics; Air pollution; Cohort; Air quality index; Logistic regression; Risk assessment; Weighting; Environmental science; Medicine; Computer science; Mathematics; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01671632,0.0004145582,0.001520258,0.00002201957,0.0005459795,0.000006765836,0.0003986076,0.0001896505,0.0001636251],"category_scores_gemma":[0.0003898398,0.0003090973,0.0002811485,0.0005745175,0.0002788107,0.0004594203,0.0002953497,0.0002114063,0.00004146502],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003824972,"about_ca_system_score_gemma":0.001600964,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01693411,"about_ca_topic_score_gemma":0.003496504,"domain_scores_codex":[0.9928254,0.001452602,0.002185496,0.0008150264,0.001066974,0.001654486],"domain_scores_gemma":[0.9955727,0.000884805,0.00134823,0.0009799134,0.0001460514,0.001068264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01821584,0.009009833,0.3116855,0.01057682,0.001108622,0.000003270966,0.03796655,0.2609301,0.005691593,0.02120581,0.2714852,0.05212088],"study_design_scores_gemma":[0.004800049,0.01117262,0.9463265,0.0008819514,0.00003688834,0.000003367427,0.003623131,0.007452112,0.0009861252,0.00342939,0.02058906,0.0006988228],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7438706,0.00035118,0.2001633,0.05089885,0.0001365272,0.003571369,0.0008172003,0.00007036083,0.0001206298],"genre_scores_gemma":[0.9570862,0.0003825244,0.02375767,0.01789177,0.00005514124,0.0002957466,0.00001910704,0.00004597561,0.0004658414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6346409,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07039444388113722,"score_gpt":0.4300831338049268,"score_spread":0.3596886899237896,"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."}}