{"id":"W2063303019","doi":"10.1289/ehp.9849","title":"Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment","year":2007,"lang":"en","type":"article","venue":"Environmental Health Perspectives","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; Canadian Institutes of Health Research; Southern California Environmental Health Sciences Center; Health Effects Institute; Health Canada; Hastings Foundation; U.S. Environmental Protection Agency","keywords":"Exposure assessment; Spatial analysis; Statistics; Residual; Health effect; Multiple exposure; Random effects model; Autocorrelation; Estimation; Econometrics; Computer science; Bayesian probability; Environmental science; Environmental health; Mathematics; Medicine; Algorithm; Artificial intelligence; Meta-analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003316518,0.0003257661,0.0004849932,0.0001261324,0.0007687298,0.00003154853,0.0001882851,0.0001490081,0.000155606],"category_scores_gemma":[0.00003548686,0.0003355157,0.0001064237,0.0002013874,0.0002922805,0.0008142408,0.0001227095,0.0004383454,0.00003052881],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009490476,"about_ca_system_score_gemma":0.0002159562,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006835036,"about_ca_topic_score_gemma":0.003736092,"domain_scores_codex":[0.99607,0.0004376843,0.000767639,0.0007742043,0.0006062696,0.001344207],"domain_scores_gemma":[0.9983956,0.0002658199,0.0003917168,0.0003072015,0.000003049812,0.0006366635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009196936,0.006754646,0.06943193,0.001422202,0.00004727193,0.00003027226,0.09968045,0.1585896,0.0009346856,0.005743301,0.005309248,0.6511367],"study_design_scores_gemma":[0.002279484,0.001900711,0.9571769,0.0001391134,0.000004751349,0.000008450847,0.0225378,0.01055015,0.00006524767,0.002846675,0.002094342,0.0003964055],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7162716,0.001819655,0.2471191,0.02847637,0.000419396,0.004138548,0.0000806954,0.0001306539,0.001544049],"genre_scores_gemma":[0.9751034,0.000264515,0.01506425,0.009160539,0.0001577562,0.00008724211,0.000048485,0.00003961781,0.00007421258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8877449,"threshold_uncertainty_score":0.9999097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03609322437058398,"score_gpt":0.3794498423613581,"score_spread":0.3433566179907742,"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."}}