{"id":"W4245163148","doi":"10.32920/14636997.v1","title":"The Role of Maps in Neighborhood-Level Heat Vulnerability Assessment for the City of Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health; Simon Fraser University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vulnerability (computing); Geography; Urban heat island; Hazard; Vulnerability assessment; Climate change; Extreme weather; Population; Index (typography); Cartography; Environmental resource management; Psychological intervention; Computer science; Environmental science; Environmental health; Meteorology; Medicine; Computer security; Ecology","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":[0.001187142,0.0003011396,0.0001809453,0.002651541,0.001051344,0.002830875,0.0003659635,0.0001918688,0.003471418],"category_scores_gemma":[0.008525383,0.0001444662,0.0002730888,0.003533397,0.0004749138,0.0006891342,0.001142402,0.0001801178,0.0002250051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006960436,"about_ca_system_score_gemma":0.005637253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7033715,"about_ca_topic_score_gemma":0.8242214,"domain_scores_codex":[0.9993375,0.0003047636,0.00003129017,0.00003908314,0.0002262048,0.00006111367],"domain_scores_gemma":[0.9971775,0.001189832,0.000200139,0.000160232,0.001043525,0.0002288307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005487421,0.0000975682,0.5709456,0.0008102396,0.0002449531,0.0007970491,0.02093751,0.115763,0.002548903,0.01570134,0.02032669,0.2512784],"study_design_scores_gemma":[0.00001881455,0.0001195066,0.7128844,0.0004104694,0.0001491851,0.0001511129,0.0368969,0.2162761,0.001607311,0.004481334,0.02686594,0.0001388872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9400601,0.000642646,0.01454056,0.001011163,0.00004854596,0.0003800191,0.006091281,0.000463382,0.03676219],"genre_scores_gemma":[0.983978,0.0002831947,0.01287935,0.00001044715,0.000005527103,0.0000697778,0.00107668,0.00003143099,0.001665639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2966285,"threshold_uncertainty_score":0.5967509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08494334860042406,"score_gpt":0.3658095805511362,"score_spread":0.2808662319507121,"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."}}