{"id":"W1968515861","doi":"10.1080/19439962.2010.487636","title":"Managing Large-Scale Multimodal Emergency Evacuations","year":2010,"lang":"en","type":"article","venue":"Journal of Transportation Safety & Security","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Transport engineering; Scheduling (production processes); Transit (satellite); Computer science; Routing (electronic design automation); Vehicle routing problem; Poison control; Population; Public transport; Operations research; Engineering; Computer network; Operations management","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001364288,0.0001162588,0.0001993694,0.0001760485,0.000581298,0.0000313269,0.00021598,0.0001402125,0.001275162],"category_scores_gemma":[0.00007136726,0.0001194102,0.0001993644,0.0004189733,0.00006862519,0.0007626613,0.000001128579,0.0004709513,0.00001093249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003169533,"about_ca_system_score_gemma":0.0002102612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002017527,"about_ca_topic_score_gemma":0.01548119,"domain_scores_codex":[0.9980499,0.00009317003,0.0008222336,0.0001447958,0.0006437845,0.0002461849],"domain_scores_gemma":[0.9983929,0.00006946416,0.0005370452,0.0001120205,0.0006872354,0.0002013541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000606337,0.001064232,0.4819088,0.0001046865,0.0002087655,0.00006927925,0.3990888,0.02916248,0.001689101,0.07560948,0.003342561,0.007145496],"study_design_scores_gemma":[0.002083452,0.00007822817,0.8270489,0.00006171064,0.0002549599,0.000004871438,0.01652024,0.002767683,0.0001856829,0.009579536,0.14098,0.0004346988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8626974,0.0001278877,0.1215942,0.005838362,0.00307614,0.0002898633,0.0002484384,0.0001101662,0.006017546],"genre_scores_gemma":[0.9919382,0.0004991498,0.006830713,0.00006460734,0.0003413967,0.000002547393,0.0001343807,0.00001319101,0.0001758121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3825686,"threshold_uncertainty_score":0.9996378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009640518955554025,"score_gpt":0.295593294097697,"score_spread":0.285952775142143,"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."}}