{"id":"W3037657161","doi":"10.1186/s12992-020-00582-3","title":"‘Calibrating to scale: a framework for humanitarian health organizations to anticipate, prevent, prepare for and manage climate-related health risks’","year":2020,"lang":"en","type":"letter","venue":"Globalization and Health","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research; Engineers Without Borders Canada; York University","funders":"Deakin University","keywords":"Climate change; Vulnerability (computing); Population health; Psychological resilience; Environmental resource management; Humanitarian aid; Population; Extreme weather; Business; Resilience (materials science); Environmental planning; Political science; Environmental health; Economic growth; Medicine; Geography; Economics; Computer security; Psychology; Ecology","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":[0.07296266,0.003665088,0.00172996,0.005402056,0.005923135,0.01425186,0.01223038,0.01027237,0.00993094],"category_scores_gemma":[0.06198396,0.001454695,0.003116584,0.005234926,0.03735276,0.02143637,0.01918184,0.01074071,0.002885246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01221129,"about_ca_system_score_gemma":0.0214028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01643487,"about_ca_topic_score_gemma":0.01616132,"domain_scores_codex":[0.9501899,0.03997907,0.002127733,0.002413513,0.003370497,0.001919319],"domain_scores_gemma":[0.9745117,0.01551332,0.00184329,0.001950003,0.004404461,0.001777116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001942131,0.00003932675,0.0008414355,0.000422559,0.00003031774,0.0002519521,0.01158427,0.007390283,0.0001520766,0.9273259,0.01860853,0.03333393],"study_design_scores_gemma":[0.00002606543,0.00007465986,0.0007085064,0.001135705,0.00002690535,0.0002002402,0.007089447,0.007528193,0.0001375343,0.7955533,0.1874305,0.00008891871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.003327453,0.00623798,0.7814599,0.09983872,0.001775291,0.001609148,0.000354023,0.0006144565,0.1047831],"genre_scores_gemma":[0.1484527,0.005504817,0.8264408,0.009586085,0.0006787132,0.003170483,0.0004799258,0.0003739486,0.005312586],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.07296266,"threshold_uncertainty_score":0.385868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09958586201992377,"score_gpt":0.3984962733185457,"score_spread":0.2989104112986219,"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."}}