{"id":"W3107359521","doi":"10.69554/fner8617","title":"A whole city approach to mass casualty planning","year":2020,"lang":"en","type":"article","venue":"","topic":"Disaster Response and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mass Casualty; Medical emergency; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001882402,0.000709259,0.0004188564,0.003157071,0.007367171,0.01466436,0.0028631,0.001777759,0.02916209],"category_scores_gemma":[0.002423778,0.0008172395,0.0008124731,0.005751531,0.006521922,0.005005542,0.007898894,0.00333215,0.002078591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01242622,"about_ca_system_score_gemma":0.01560877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08213314,"about_ca_topic_score_gemma":0.1730876,"domain_scores_codex":[0.9983975,0.0007469358,0.00005500911,0.0002427921,0.0002794435,0.0002782986],"domain_scores_gemma":[0.9988145,0.0003017041,0.0001048793,0.0001608926,0.0002515007,0.0003665701],"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":[0.00006899362,0.000229585,0.007700407,0.000409448,0.0001034695,0.001667213,0.02058549,0.03728697,0.0005431527,0.7675063,0.05237444,0.1115246],"study_design_scores_gemma":[0.00004292667,0.000147324,0.00911497,0.0005196949,0.00009211725,0.0008170509,0.07768115,0.02310063,0.0007491555,0.1929928,0.694643,0.00009901814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04053465,0.002320797,0.2396166,0.02015512,0.0007479497,0.001021239,0.0008658819,0.0007876524,0.6939501],"genre_scores_gemma":[0.6125157,0.005758257,0.2244122,0.001962018,0.0002321044,0.0007371131,0.0007599489,0.0004885685,0.153134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08213314,"threshold_uncertainty_score":0.1633102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2673893563616042,"score_gpt":0.4567200234758159,"score_spread":0.1893306671142117,"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."}}