{"id":"W2022596591","doi":"10.4081/gh.2013.85","title":"Identifying inequitable exposure to toxic air pollution in racialized and low-income neighbourhoods to support pollution prevention","year":2013,"lang":"en","type":"article","venue":"Geospatial health","topic":"Environmental Justice and Health Disparities","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Public Health; Toronto Metropolitan University","funders":"","keywords":"Environmental justice; Environmental health; Population; Poverty; Socioeconomic status; Hazard; Air pollution; Geography; Toxicology; Medicine; Biology; Ecology; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237053,0.0001461238,0.0002896417,0.0002219724,0.0007433909,0.00009092906,0.0001356724,0.0001307436,0.0004487093],"category_scores_gemma":[0.000109523,0.0001654952,0.00003781922,0.0004072007,0.0000600118,0.000627534,0.000106743,0.0001536843,0.0002153428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007602745,"about_ca_system_score_gemma":0.0004273688,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06962898,"about_ca_topic_score_gemma":0.1220158,"domain_scores_codex":[0.9975396,0.0003312191,0.0005276464,0.000340966,0.0004262101,0.0008343559],"domain_scores_gemma":[0.9990755,0.0000291471,0.0001417692,0.0001530341,0.00003081843,0.0005696804],"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":[0.0005756108,0.001282506,0.2416451,0.004627171,0.00004187491,0.00001474917,0.2429913,0.002318718,0.001806972,0.03479254,0.01746246,0.452441],"study_design_scores_gemma":[0.0008048688,0.0009407711,0.9805335,0.0004856389,0.000009288749,0.000001124755,0.01188106,0.00003635704,0.00004809152,0.001948411,0.003045228,0.0002656111],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698107,0.0004034956,0.002494935,0.02384317,0.0005815556,0.001944854,0.00002248257,0.00006776205,0.0008310896],"genre_scores_gemma":[0.9880989,0.0006183631,0.001009852,0.008691539,0.0003180005,0.0001784875,0.00002212072,0.0000152566,0.001047522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7388885,"threshold_uncertainty_score":0.9365665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252236475473587,"score_gpt":0.3452772444245659,"score_spread":0.3200535968772071,"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."}}