{"id":"W2784723902","doi":"10.17269/cjph.108.6166","title":"Socio-economic inequalities in exposure to industrial air pollution emissions in Quebec public schools","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Environmental Justice and Health Disparities","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Montréal","funders":"","keywords":"Neighbourhood (mathematics); Particulates; Air pollution; Census; Environmental science; Geography; Mathematics; Demography; Population; Chemistry; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007679702,0.0003836491,0.0005374958,0.002060115,0.004100021,0.002230595,0.001586235,0.001053291,0.007819499],"category_scores_gemma":[0.002100269,0.0003516875,0.0008671763,0.004687628,0.00114554,0.0009197083,0.001624886,0.001414772,0.0004417672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03183407,"about_ca_system_score_gemma":0.02322349,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954229,"about_ca_topic_score_gemma":0.9977859,"domain_scores_codex":[0.9983888,0.000173685,0.00006954975,0.000207634,0.0002909313,0.0008695241],"domain_scores_gemma":[0.9975744,0.0002175037,0.0005748927,0.00006954168,0.000762013,0.000801677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001026492,0.0001805158,0.9887638,0.00003409836,0.0001080307,0.00009144969,0.002107279,0.000317779,0.0001253776,0.0008384605,0.00228527,0.005045236],"study_design_scores_gemma":[0.00000493305,0.000010936,0.9969984,0.00003700917,0.00002078161,0.000006976181,0.001863549,0.0002882762,0.00002066621,0.0000491871,0.0006917102,0.000007598225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907759,0.0008369307,0.00008525435,0.001295732,0.00002288501,0.00002346253,0.002164512,0.000009987725,0.00478533],"genre_scores_gemma":[0.9978817,0.000176228,0.00003021469,0.00009586891,0.000006377333,0.000009640622,0.0004170266,0.000002682038,0.001380152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03183407,"threshold_uncertainty_score":0.2309734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1427923091801452,"score_gpt":0.3581113248249655,"score_spread":0.2153190156448203,"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."}}