{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002470666,0.0002942386,0.000773738,0.0004307843,0.0002044564,0.00007955485,0.0001898785,0.0002097998,0.000574003],"category_scores_gemma":[0.00137609,0.0002071831,0.00007434221,0.0006658843,0.0004015721,0.0003200501,0.0002116849,0.0005299585,0.00007189367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002215199,"about_ca_system_score_gemma":0.00296751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00378434,"about_ca_topic_score_gemma":0.0003781474,"domain_scores_codex":[0.9961106,0.0003194206,0.0007573771,0.0005800695,0.001284259,0.0009483314],"domain_scores_gemma":[0.994559,0.000159194,0.0001427019,0.0004927705,0.0002675396,0.004378803],"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":[0.0002243011,0.0004648332,0.01374887,0.001049683,0.0001805641,0.0001455317,0.2354128,5.262594e-7,0.00003918744,0.002036677,0.3404635,0.4062335],"study_design_scores_gemma":[0.008406939,0.001720472,0.003195423,0.001609255,0.00003376318,0.00112334,0.2360476,0.0009890787,0.000003425399,0.0003978408,0.7460926,0.0003803085],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6635653,0.004325215,0.006335243,0.3086667,0.001086783,0.0009909892,0.00001171869,0.0003566957,0.01466143],"genre_scores_gemma":[0.9679986,0.0006531486,0.0002325479,0.02323029,0.002213991,0.0000699578,0.0001154725,0.00007068321,0.005415313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4058532,"threshold_uncertainty_score":0.8448682,"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."}}