{"id":"W2046349969","doi":"10.1287/trsc.1100.0339","title":"A Tactical Planning Model for Railroad Transportation of Dangerous Goods","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University; Memorial University of Newfoundland","funders":"","keywords":"Train; Dangerous goods; Transportation planning; Transport engineering; Operations research; Truck; Genetic algorithm; Flow network; Population; Yard; Hazardous waste; Component (thermodynamics); Engineering; Computer science; Mathematical optimization; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003373851,0.0001311506,0.0003084947,0.0004445715,0.0003006982,0.00009134816,0.0008079929,0.00008884717,0.00008460284],"category_scores_gemma":[0.0006713363,0.0001047094,0.0002058815,0.001965103,0.0005956375,0.001072905,0.000002093198,0.0001664072,0.000009999636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001099784,"about_ca_system_score_gemma":0.000311163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004900058,"about_ca_topic_score_gemma":0.000600269,"domain_scores_codex":[0.9963496,0.00002062275,0.0009216969,0.0005841563,0.001823997,0.0002999645],"domain_scores_gemma":[0.9977144,0.0005580953,0.0003666353,0.0004498684,0.000734354,0.0001766695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005736207,0.0003386132,0.07338382,0.0000395131,0.00003937995,0.00001029229,0.04402387,0.293194,0.3551362,0.1094244,0.0004208438,0.1234155],"study_design_scores_gemma":[0.0009437483,0.00010995,0.3110816,0.00001566014,0.0001157535,0.000001491587,0.001518618,0.611042,0.01808422,0.05530766,0.001431835,0.0003473809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5442548,0.000008875981,0.4547735,0.0003353751,0.0001347681,0.0001332637,0.00008700418,0.0000199532,0.000252477],"genre_scores_gemma":[0.95367,0.000005413558,0.04595868,0.00006439425,0.00002524796,0.0000210973,0.00002808628,0.000007659662,0.0002194174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4094152,"threshold_uncertainty_score":0.4269924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.132769971646539,"score_gpt":0.4402819861719345,"score_spread":0.3075120145253954,"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."}}