{"id":"W4391641985","doi":"10.1101/2024.02.07.24302381","title":"Air Pollution and Children’s Health Inequalities","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Agence Nationale de la Recherche","keywords":"Vulnerability (computing); Inequality; Environmental health; Low income; Air pollution; Pollution; Intervention (counseling); Psychology; Geography; Medicine; Demographic economics; Economics; Computer science; Nursing; Mathematics","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":[0.001267506,0.0002584762,0.0003810049,0.0009819611,0.0002774085,0.001438154,0.0002873903,0.0005273294,0.01359073],"category_scores_gemma":[0.004760113,0.00008820892,0.0004026144,0.00167367,0.0006094464,0.0004655695,0.0009593095,0.0006272882,0.0005721737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001444514,"about_ca_system_score_gemma":0.0007075911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03936383,"about_ca_topic_score_gemma":0.01961648,"domain_scores_codex":[0.9989988,0.0004039712,0.00004108873,0.0001516204,0.0001843047,0.0002201988],"domain_scores_gemma":[0.9947647,0.00270501,0.00172845,0.0002369381,0.0002612775,0.0003035969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002114797,0.0001314446,0.9209916,0.0002841385,0.0004286148,0.0002642131,0.0007126114,0.01035311,0.0004729371,0.0248662,0.006468247,0.03481536],"study_design_scores_gemma":[0.00001869883,0.0001113411,0.9746752,0.0001218158,0.0000815629,0.00007771513,0.001021208,0.003795488,0.0002923226,0.0099473,0.009843235,0.00001416245],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594997,0.005958105,0.001843527,0.007847616,0.0001084301,0.00002465668,0.01117969,0.00006493134,0.01347346],"genre_scores_gemma":[0.9957572,0.0008928088,0.0002213673,0.0001618199,0.00005906725,0.00001136632,0.001319981,0.000004715244,0.001571672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03936383,"threshold_uncertainty_score":0.07826942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04272109621793811,"score_gpt":0.3184994142596014,"score_spread":0.2757783180416633,"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."}}