{"id":"W2945965952","doi":"10.1177/0361198119848409","title":"Vulnerability Assessment during Mass Evacuation: Integrated Microsimulation-Based Evacuation Modeling Approach","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Marine Environmental Observation Prediction and Response Network","keywords":"Microsimulation; Vulnerability (computing); Vulnerability assessment; Flood myth; Computer science; Social vulnerability; Bayesian network; Risk assessment; Transport engineering; Risk analysis (engineering); Operations research; Geography; Business; Computer security; Engineering; Psychological resilience","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000539363,0.0008401101,0.0007286522,0.0007185459,0.0005140672,0.000790056,0.0009846424,0.0009629288,0.001179561],"category_scores_gemma":[0.001299334,0.0004852916,0.001016833,0.0003766237,0.0006472767,0.0007014461,0.001149382,0.0006912809,0.000108467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001703665,"about_ca_system_score_gemma":0.001828595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0617069,"about_ca_topic_score_gemma":0.02671638,"domain_scores_codex":[0.9997715,0.00008277865,0.000008779024,0.00003909982,0.00004367752,0.00005412292],"domain_scores_gemma":[0.9995426,0.0002493325,0.00007695954,0.00001527035,0.00008365168,0.00003218582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004712342,0.000004390543,0.000260047,0.000003753991,0.000008423843,0.00001047325,0.0000122983,0.9981136,0.0001022368,0.001003428,0.00002464814,0.0004519297],"study_design_scores_gemma":[0.000001341724,0.000005853835,0.00008156102,0.00000148718,0.000003630585,0.000002492045,0.000008715561,0.9993095,0.00004489817,0.0004625883,0.00007560878,0.000002308934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2484639,0.0003750756,0.7368825,0.0006404249,0.00004760357,0.0001445664,0.0004228467,0.0002709915,0.01275198],"genre_scores_gemma":[0.9727334,0.0002445519,0.02308299,0.00005037193,0.00001956272,0.0001767635,0.0001473701,0.00003769713,0.003507147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0617069,"threshold_uncertainty_score":0.1226954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06280798376749332,"score_gpt":0.3747452740669623,"score_spread":0.311937290299469,"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."}}