{"id":"W2082624981","doi":"10.1016/j.tre.2015.02.003","title":"Planning and managing intermodal transportation of hazardous materials with capacity selection and congestion","year":2015,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Hazardous waste; Truck; Traffic congestion; Transport engineering; Yard; Flow network; Selection (genetic algorithm); Computer science; Genetic algorithm; Operations research; Engineering; Waste management; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004094967,0.0002236356,0.0006673087,0.0003549331,0.000217068,0.00011215,0.0001329659,0.0001076364,0.00005816465],"category_scores_gemma":[0.000224292,0.0001737098,0.0000532451,0.000868972,0.0004477784,0.0004324275,0.000001820145,0.0002426191,0.000002634058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001944507,"about_ca_system_score_gemma":0.00009321344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007246756,"about_ca_topic_score_gemma":0.005274646,"domain_scores_codex":[0.996209,0.0003504486,0.001214196,0.0005898048,0.001355211,0.0002813107],"domain_scores_gemma":[0.9973634,0.0004556779,0.0004282131,0.0001982218,0.001281854,0.0002726291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002854878,0.0004564659,0.7002509,0.01039212,0.0006517941,0.0002504728,0.02084097,0.01950546,0.002193742,0.1212059,0.001531975,0.1198653],"study_design_scores_gemma":[0.001846771,0.0008078399,0.9588588,0.002514082,0.0006615844,0.00001032754,0.002271216,0.001510253,0.0006556111,0.02509182,0.005264578,0.0005071364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358899,0.009215861,0.05169835,0.00163685,0.0000750254,0.000891497,0.0004443094,0.00004323801,0.000104983],"genre_scores_gemma":[0.9756111,0.02227288,0.001514467,0.00006188988,0.00002512324,0.00005330643,0.0003990268,0.00001637969,0.00004580863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2586079,"threshold_uncertainty_score":0.708368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3429227397102683,"score_gpt":0.4497684245023076,"score_spread":0.1068456847920393,"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."}}