{"id":"W2129649894","doi":"10.1080/15287390903129408","title":"Predicting Personal Nitrogen Dioxide Exposure in an Elderly Population: Integrating Residential Indoor and Outdoor Measurements, Fixed-Site Ambient Pollution Concentrations, Modeled Pollutant Levels, and Time–Activity Patterns","year":2009,"lang":"en","type":"article","venue":"Journal of Toxicology and Environmental Health","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Aecom (Canada); Environment and Climate Change Canada; Hamilton Health Sciences; Health Canada; Custom Security Industries (Canada)","funders":"Canadian Institutes of Health Research","keywords":"Nitrogen dioxide; Pollutant; Environmental science; Air pollution; Pollution; Environmental epidemiology; Exposure assessment; Population; Particulates; Environmental health; Atmospheric sciences; Environmental chemistry; Meteorology; Geography; Ecology; Chemistry; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005034308,0.0004890481,0.0003998124,0.0004123321,0.0002217897,0.0005989139,0.0004066853,0.0003029694,0.0002383746],"category_scores_gemma":[0.001020994,0.0002197245,0.0004644775,0.0005005331,0.0001604825,0.0002713794,0.0004492298,0.0002725296,0.0001022969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009547576,"about_ca_system_score_gemma":0.001294583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3828596,"about_ca_topic_score_gemma":0.4354681,"domain_scores_codex":[0.9998552,0.00003277409,0.0000103902,0.00005273158,0.00002591079,0.00002303879],"domain_scores_gemma":[0.9997315,0.00007564273,0.00005677369,0.00001868659,0.00006370018,0.0000538077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006966122,0.00003269094,0.9917339,0.000008603894,0.00008932633,0.00002910955,0.00004866343,0.003235142,0.0002807484,0.00001029734,0.00006596184,0.004395792],"study_design_scores_gemma":[0.00000854907,0.000107525,0.9413405,0.000008310449,0.0001255321,0.00004965144,0.0002571954,0.05758365,0.0002315411,0.00007957746,0.000198999,0.00000903351],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989072,0.0001003035,0.0006105218,0.00001947441,0.000001656964,0.000008125251,0.0002144978,0.00001180306,0.000126459],"genre_scores_gemma":[0.998293,0.00009429453,0.001022069,0.000009266461,0.000002531802,0.000005669744,0.0004289667,0.000002215392,0.0001420493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3828596,"threshold_uncertainty_score":0.7612623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03634240011793728,"score_gpt":0.2964189507940573,"score_spread":0.26007655067612,"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."}}