{"id":"W2972463787","doi":"10.14288/1.0380851","title":"Mapping spatial patterns in vulnerability to climate change-related health hazards : 2020 Report","year":2019,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Vulnerability (computing); Climate change; Geography; Spatial epidemiology; Environmental resource management; Environmental planning; Environmental health; Environmental science; Computer science; Computer security; Medicine; Epidemiology; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068678,0.0005366274,0.0001546934,0.002424223,0.0002575014,0.0009504465,0.0004124183,0.0003121322,0.005589474],"category_scores_gemma":[0.001716661,0.0001965719,0.0004751916,0.003266831,0.00008762775,0.0004161649,0.0007895808,0.0002089514,0.001915139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002209496,"about_ca_system_score_gemma":0.003319738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3861506,"about_ca_topic_score_gemma":0.439037,"domain_scores_codex":[0.9997513,0.00002265184,0.00001776246,0.00002295306,0.0001291408,0.00005618652],"domain_scores_gemma":[0.9988159,0.00007340039,0.0001048046,0.00004563188,0.0008084858,0.0001518022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005852329,0.0002245637,0.4787905,0.001265125,0.000486071,0.0007125011,0.001073924,0.02597401,0.004165833,0.002034337,0.3020281,0.1826599],"study_design_scores_gemma":[0.00004378961,0.0001535275,0.9058816,0.00023038,0.000171974,0.0001746543,0.001798605,0.01062308,0.001615853,0.0006328564,0.0786135,0.00006011324],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2811578,0.001102811,0.0057762,0.001780551,0.0001161097,0.0004717569,0.6691251,0.0007773545,0.03969236],"genre_scores_gemma":[0.5731466,0.001762868,0.01696991,0.0001693466,0.00004102552,0.0007428128,0.38897,0.0001469043,0.01805047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3861506,"threshold_uncertainty_score":0.7678058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120063401609513,"score_gpt":0.2367108880321456,"score_spread":0.2155102540160505,"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."}}