{"id":"W2006941854","doi":"10.1017/dmp.2015.14","title":"Mapping Medical Disasters: Ebola Makes Old Lessons, New","year":2015,"lang":"en","type":"article","venue":"Disaster Medicine and Public Health Preparedness","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Preparedness; Economic shortage; Disaster medicine; Context (archaeology); Public health; Disaster planning; Scarcity; Medical emergency; Disaster preparedness; Coronavirus disease 2019 (COVID-19); Disaster response; Emergency management; Political science; Geography; Environmental planning; Medicine; Poison control; Suicide prevention; Infectious disease (medical specialty); Disease; Nursing; Government (linguistics)","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":[0.009644053,0.001010412,0.0007668271,0.003857498,0.002612654,0.01005675,0.00185648,0.004049695,0.005620783],"category_scores_gemma":[0.01762684,0.0004656572,0.0005095874,0.003876041,0.009066687,0.02083114,0.005555926,0.005221101,0.001222914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002129988,"about_ca_system_score_gemma":0.003595549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006931443,"about_ca_topic_score_gemma":0.0151264,"domain_scores_codex":[0.9969779,0.001630689,0.0001930616,0.0002062254,0.0007815128,0.0002105658],"domain_scores_gemma":[0.985593,0.008778305,0.0007253886,0.001043704,0.002828523,0.001031047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006291035,0.00005995516,0.006446771,0.003489781,0.00010813,0.0008619997,0.02103596,0.001522808,0.000815424,0.0781426,0.2371279,0.6503258],"study_design_scores_gemma":[0.000007727167,0.00006751268,0.0046876,0.003606697,0.00004370018,0.001227187,0.05127415,0.0004745756,0.0004981181,0.0983964,0.8396211,0.00009522339],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.009653275,0.3552783,0.01835256,0.5634128,0.01845223,0.00006338784,0.0004294946,0.0002488678,0.03410914],"genre_scores_gemma":[0.1914583,0.6632577,0.0370828,0.05723304,0.03014836,0.00009362936,0.0006229014,0.0002727199,0.0198306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01005675,"threshold_uncertainty_score":0.05100322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2643177476523777,"score_gpt":0.4448040748048792,"score_spread":0.1804863271525015,"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."}}