{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00555578,0.0001092234,0.0003326551,0.00074087,0.001403684,0.0004825399,0.0006058302,0.0002122802,0.0003176976],"category_scores_gemma":[0.002630942,0.0001185502,0.00005734461,0.0001401017,0.0002605001,0.001548347,0.00002837285,0.0006489843,0.00002635462],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.007528285,"about_ca_system_score_gemma":0.04489324,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8877133,"about_ca_topic_score_gemma":0.9950265,"domain_scores_codex":[0.9971303,0.0006378561,0.0007581909,0.000150841,0.0002451467,0.001077689],"domain_scores_gemma":[0.9959663,0.00007835097,0.0004815574,0.0002106887,0.00004761045,0.003215548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006665207,0.00003489808,0.8660638,0.00004572174,0.000008011582,0.00002703164,0.041203,0.00005250371,7.556155e-7,0.04043696,0.02258108,0.02953961],"study_design_scores_gemma":[0.0006797086,0.0001133783,0.5867366,0.0002603609,0.000001862463,0.000002551242,0.165883,0.000001274751,3.181109e-7,0.0004801574,0.2457047,0.0001361136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.647081,0.0004960852,0.00001152796,0.3500178,0.0007118065,0.0001940419,0.00003873133,0.000003625741,0.00144547],"genre_scores_gemma":[0.9922595,0.0004225071,0.00005636418,0.005471165,0.0008626566,0.000008832456,0.000004182272,0.000010412,0.0009043795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3451785,"threshold_uncertainty_score":0.9998963,"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."}}