{"id":"W2473872738","doi":"10.1111/cag.12282","title":"Using multiple disparate data sources to map heat vulnerability: Vancouver case study","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Simon Fraser University","funders":"Washington State University","keywords":"Vulnerability (computing); Extreme heat; Psychological intervention; Urban heat island; Environmental planning; Geography; Term (time); Environmental resource management; Climate change; Risk analysis (engineering); Business; Environmental science; Computer science; Meteorology; Computer security; Medicine; Ecology","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":[0.001540739,0.0003483858,0.0002736632,0.002852053,0.002812396,0.002184679,0.0008217969,0.0007383269,0.001698682],"category_scores_gemma":[0.006319004,0.0002720578,0.0002427929,0.008055164,0.0005744337,0.0007407653,0.001784163,0.0007617696,0.000225261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006497947,"about_ca_system_score_gemma":0.004553132,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7894427,"about_ca_topic_score_gemma":0.8848258,"domain_scores_codex":[0.9985194,0.0005683163,0.00008861242,0.0001290798,0.0004840864,0.0002105684],"domain_scores_gemma":[0.9968727,0.0011198,0.0002133896,0.0002272235,0.001273547,0.000293313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002921351,0.0004438252,0.7671576,0.0005931571,0.0002745906,0.01256248,0.02046449,0.02161566,0.002172721,0.0036902,0.01351494,0.1572183],"study_design_scores_gemma":[0.000164376,0.000338394,0.7104282,0.0009764499,0.0002998411,0.00497407,0.1426308,0.07878602,0.003056737,0.004286751,0.05379555,0.0002628355],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691775,0.00046657,0.004331361,0.001367873,0.00002439672,0.0005483779,0.002639246,0.00006030352,0.02138451],"genre_scores_gemma":[0.9878315,0.0005243349,0.00738007,0.000107105,0.000009030934,0.0001770295,0.001092322,0.0000187274,0.002859719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2105573,"threshold_uncertainty_score":0.4235947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06927412015255961,"score_gpt":0.289456582081302,"score_spread":0.2201824619287424,"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."}}