{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001000722,0.0009105478,0.0007568232,0.0009278335,0.000687118,0.001692972,0.001589385,0.001763568,0.006336237],"category_scores_gemma":[0.001284719,0.0005435072,0.0009057033,0.001588675,0.001012219,0.001233698,0.0007779066,0.00122852,0.0005694094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002225427,"about_ca_system_score_gemma":0.002860417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02262766,"about_ca_topic_score_gemma":0.02181634,"domain_scores_codex":[0.9995111,0.0001697477,0.00002051778,0.0001153449,0.0001003246,0.0000829525],"domain_scores_gemma":[0.9994934,0.0002893812,0.00006974366,0.0000206867,0.00008141281,0.00004552478],"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":[0.00001483975,0.00001989695,0.0001101811,0.00002403573,0.00001041646,0.00008860288,0.00003089577,0.9820465,0.000175503,0.01500243,0.0003804096,0.002096354],"study_design_scores_gemma":[0.00001513223,0.00002636914,0.00006816076,0.000006351448,0.000008702705,0.00002726967,0.00002937709,0.9922575,0.00008572746,0.006327861,0.001142021,0.000005513521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06281593,0.0003764682,0.9080811,0.00126638,0.00007744705,0.0003172998,0.001478761,0.000302819,0.02528377],"genre_scores_gemma":[0.6970202,0.0007808001,0.2761588,0.0002128218,0.00005439726,0.001187743,0.001112946,0.00007986163,0.02339235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02262766,"threshold_uncertainty_score":0.04499191,"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."}}