{"id":"W1973963219","doi":"10.1109/icsssm.2011.5959522","title":"Online routing of hazardous materials transportation based on risk equity","year":2011,"lang":"en","type":"article","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing University of Chemical Technology; National Natural Science Foundation of China; Beijing University of Technology; Ryerson University","keywords":"Hazardous waste; Equity (law); Computer science; Node (physics); Transport engineering; Flow network; Risk analysis (engineering); Operations research; Business; Engineering; Mathematical optimization; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0007499848,0.0008367199,0.001344061,0.0007167831,0.0007512706,0.001317558,0.001425301,0.001225265,0.004606093],"category_scores_gemma":[0.002424483,0.000582037,0.0006896483,0.0009419427,0.0006933662,0.002038306,0.001129053,0.0006806614,0.0003406125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001733117,"about_ca_system_score_gemma":0.001333776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00495851,"about_ca_topic_score_gemma":0.003705519,"domain_scores_codex":[0.9992828,0.0002456532,0.00002528617,0.0001533604,0.0001442025,0.0001486707],"domain_scores_gemma":[0.9989321,0.0005449313,0.0001950254,0.00008726792,0.0001065104,0.0001341554],"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":[0.0001558399,0.00009887041,0.0006414251,0.00005231783,0.00003598342,0.00006197049,0.0000561313,0.9451067,0.001122842,0.01675354,0.001779733,0.03413456],"study_design_scores_gemma":[0.00001050415,0.00003277707,0.00007618282,0.000002689431,0.000005914523,0.00001500966,0.00001220124,0.9935547,0.0002305712,0.005655793,0.0004004202,0.000003393747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1464017,0.0004196838,0.8394621,0.0006166419,0.0000819578,0.0002131479,0.0001960504,0.0004437079,0.01216491],"genre_scores_gemma":[0.8818967,0.0003271938,0.1094748,0.0001407009,0.00004674115,0.0001418376,0.0002804816,0.00008078518,0.007610658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00495851,"threshold_uncertainty_score":0.01540893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06880667339669169,"score_gpt":0.2959013792763289,"score_spread":0.2270947058796372,"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."}}