{"id":"W4309675585","doi":"10.1016/j.envint.2022.107633","title":"The Canadian Environmental Quality Index (Can-EQI): Development and calculation of an index to assess spatial variation of environmental quality in Canada’s 30 largest cities","year":2022,"lang":"en","type":"article","venue":"Environment International","topic":"Environmental Justice and Health Disparities","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Public Health Agency of Canada","funders":"Dalhousie University; Public Health Agency; Public Health Agency of Canada","keywords":"Index (typography); Environmental quality; Quality (philosophy); Spatial variability; Environmental science; Geography; Variation (astronomy); Environmental protection; Statistics; Mathematics; Ecology; Computer science; Biology","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":[0.005787656,0.0009662598,0.001302946,0.01059062,0.002361208,0.002688747,0.003099236,0.0005560308,0.003145097],"category_scores_gemma":[0.02228306,0.0004755224,0.002575504,0.02580905,0.0006294186,0.001074515,0.002626864,0.001191056,0.000567393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03356507,"about_ca_system_score_gemma":0.06734208,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9709619,"about_ca_topic_score_gemma":0.9826424,"domain_scores_codex":[0.9943327,0.0006302179,0.0009199182,0.0007861914,0.002875516,0.0004555332],"domain_scores_gemma":[0.9822907,0.001686297,0.00189898,0.0007143429,0.01270079,0.0007089009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002534287,0.00008929488,0.6972603,0.006619166,0.0024229,0.0001343719,0.001229609,0.006676805,0.0003248002,0.005863582,0.1903575,0.08876823],"study_design_scores_gemma":[0.0001016362,0.00003270764,0.9072548,0.001723096,0.0005765164,0.00007453001,0.001245447,0.006143181,0.0004608175,0.001580927,0.08066987,0.000136544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06465699,0.003440375,0.0121512,0.001841894,0.0001262629,0.001868763,0.9014228,0.000383319,0.01410839],"genre_scores_gemma":[0.2925332,0.003855512,0.05688901,0.000870713,0.00004781565,0.006955174,0.6363488,0.0001443617,0.002355353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03356507,"threshold_uncertainty_score":0.2435327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0321825189585086,"score_gpt":0.3006829170790311,"score_spread":0.2685003981205225,"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."}}