{"id":"W2027580141","doi":"10.1016/j.tre.2011.06.001","title":"A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials","year":2011,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":136,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Memorial University of Newfoundland","funders":"U.S. Department of Transportation; U.S. Department of Energy","keywords":"Train; Hazardous waste; Tabu search; Truck; Transport engineering; Scheduling (production processes); Routing (electronic design automation); Computer science; Operations research; Route planning; Transportation planning; Engineering; Automotive engineering; Computer network; Operations management; Waste management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003115435,0.002053872,0.002845762,0.001963742,0.0009153765,0.003041902,0.003468323,0.003349642,0.006335075],"category_scores_gemma":[0.003512994,0.001743799,0.001695335,0.00267945,0.001132159,0.002684538,0.001602632,0.002167373,0.0007628651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003332739,"about_ca_system_score_gemma":0.003727452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05100541,"about_ca_topic_score_gemma":0.03274615,"domain_scores_codex":[0.9986746,0.0005116776,0.00007096711,0.0002178969,0.00030765,0.0002172646],"domain_scores_gemma":[0.9981806,0.001164959,0.0001803894,0.00004428516,0.0003132889,0.0001164073],"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":[0.00001034468,0.00001448249,0.00009905872,0.00001702781,0.0000192122,0.0000173633,0.000007400623,0.9969562,0.00003938029,0.00131034,0.0001045233,0.001404784],"study_design_scores_gemma":[0.000006257808,0.00001388295,0.00004647081,0.000002981548,0.000008728602,0.000002741356,0.000005839314,0.9990987,0.00002175245,0.0006881512,0.0001011352,0.000003494984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07721307,0.001219501,0.8996272,0.001046514,0.0002157661,0.00040298,0.001825871,0.0005440199,0.01790507],"genre_scores_gemma":[0.8252099,0.001421602,0.1528407,0.0002481722,0.0001002006,0.001273005,0.001296804,0.0001392748,0.0174704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05100541,"threshold_uncertainty_score":0.1014171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3712277821255654,"score_gpt":0.4621481738678355,"score_spread":0.09092039174227012,"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."}}