{"id":"W2911888399","doi":"10.1155/2019/9848603","title":"Modeling a Risk-Based Dynamic Bus Schedule Problem under No-Notice Evacuation Incorporated with Dynamics of Disaster, Supply, and Demand Conditions","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Schedule; Emergency evacuation; Operations research; Computer science; Notice; Time horizon; Population; Transport engineering; Resource allocation; Engineering; Mathematical optimization; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001011465,0.001310049,0.001176175,0.0006435531,0.0005532493,0.001820099,0.001462119,0.00186533,0.003645163],"category_scores_gemma":[0.002222839,0.0008325732,0.001234189,0.000635563,0.0008743744,0.001428921,0.001328357,0.001638546,0.0002610683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001870768,"about_ca_system_score_gemma":0.002324636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02326414,"about_ca_topic_score_gemma":0.01432982,"domain_scores_codex":[0.9994005,0.0001969275,0.00002411508,0.0001539093,0.00007763218,0.0001469081],"domain_scores_gemma":[0.9989133,0.000616936,0.0002037198,0.00002735811,0.0001233677,0.0001152897],"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":[0.00001878161,0.00001495811,0.0003477248,0.00003159246,0.00001505571,0.00006972811,0.00002936838,0.9896387,0.0001452325,0.008355895,0.0002441438,0.001088839],"study_design_scores_gemma":[0.000004348708,0.00001250444,0.00007392096,0.000003216435,0.000005971148,0.000006744551,0.00001958895,0.9973456,0.0000287639,0.002345132,0.0001509664,0.00000318964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1698294,0.000981199,0.8062122,0.00164202,0.0001827924,0.0002114472,0.0007998248,0.0002396605,0.01990147],"genre_scores_gemma":[0.9535483,0.0008391395,0.0336522,0.0001121173,0.00007633572,0.000291613,0.0003893696,0.00006906112,0.01102194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02326414,"threshold_uncertainty_score":0.04625744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003808694791370675,"score_gpt":0.2131028913242744,"score_spread":0.2092941965329037,"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."}}