{"id":"W2919108019","doi":"10.1093/ije/dyz008","title":"How urban characteristics affect vulnerability to heat and cold: a multi-country analysis","year":2019,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":274,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Health Canada","funders":"Terveyden Tutkimuksen Toimikunta; Medical Research Council; National Health Research Institutes; Academy of Finland; Natural Environment Research Council; Sight Research UK","keywords":"Metropolitan area; Geography; Urban heat island; Distributed lag; Confidence interval; Multivariate statistics; Index (typography); Population; Gross domestic product; Vulnerability (computing); Psychological intervention; Environmental health; Effect modification; Inequality; Demography; Socioeconomics; Medicine; Economics; Economic growth; Econometrics; Statistics; Meteorology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01296157,0.001269572,0.002614496,0.003037405,0.0008288374,0.002193789,0.001536129,0.001172739,0.002883918],"category_scores_gemma":[0.01484571,0.001061255,0.01832426,0.005478352,0.0006749446,0.001003272,0.002773816,0.001881447,0.0002822051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008104773,"about_ca_system_score_gemma":0.0009299669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01756449,"about_ca_topic_score_gemma":0.01442785,"domain_scores_codex":[0.9910175,0.005785618,0.0009438027,0.001350233,0.0003467174,0.0005560917],"domain_scores_gemma":[0.9847388,0.008650891,0.002906039,0.002197725,0.0009934691,0.0005130427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001365107,0.00008323802,0.8675609,0.002528402,0.1166324,0.000309651,0.0002782996,0.004437617,0.0002375037,0.0003722601,0.001545353,0.004649277],"study_design_scores_gemma":[0.0004158584,0.0005040109,0.8053171,0.001601103,0.1717313,0.0005455742,0.00104636,0.01344162,0.0003781847,0.001284029,0.003602767,0.0001321569],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9095664,0.06058114,0.01236143,0.001219671,0.0002637983,0.0002601299,0.0143476,0.000118755,0.001281042],"genre_scores_gemma":[0.9910081,0.003531974,0.002007021,0.000220943,0.00004172735,0.0001733414,0.002827768,0.00003224519,0.0001569367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01756449,"threshold_uncertainty_score":0.06854814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0684977173951135,"score_gpt":0.3821754128406404,"score_spread":0.3136776954455269,"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."}}