{"id":"W4401360965","doi":"10.1016/j.ejor.2024.08.006","title":"A value-at-risk based approach to the routing problem of multi-hazmat railcars","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Memorial University of Newfoundland","funders":"","keywords":"Consolidation (business); Computer science; Operations research; Routing (electronic design automation); Heuristic; Vehicle routing problem; Mathematical optimization; Risk analysis (engineering); Engineering; Business; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06320465,0.0001159994,0.0002746811,0.0008265061,0.0006282122,0.0007499541,0.00154873,0.00002218319,0.0002928055],"category_scores_gemma":[0.006789071,0.00005987503,0.0003311478,0.002234242,0.0001762567,0.0003129514,0.0003341378,0.0007557549,0.0006392758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037516,"about_ca_system_score_gemma":0.0005357474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002218308,"about_ca_topic_score_gemma":0.000009737091,"domain_scores_codex":[0.9874915,0.005863787,0.001282682,0.0003480673,0.004713351,0.0003005918],"domain_scores_gemma":[0.9939257,0.003034062,0.0002188125,0.000407087,0.002198879,0.0002154976],"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":[0.0003388665,0.0002892441,0.002846766,0.0000224045,0.0002411202,0.0001643635,0.006997341,0.8000128,0.003825231,0.008418939,0.05960592,0.117237],"study_design_scores_gemma":[0.0009364799,0.0005628524,0.02235326,0.000226384,0.00005942485,0.0001278439,0.002790511,0.7900321,0.0009336437,0.001250104,0.1805043,0.0002230656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.230984,0.004106223,0.6302926,0.04751592,0.0004931365,0.0009799289,0.0001319007,0.00002977151,0.08546652],"genre_scores_gemma":[0.958985,0.0000892637,0.03540135,0.0001205781,0.0002940272,0.000002813016,0.000002847433,0.00002135866,0.005082815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7280009,"threshold_uncertainty_score":0.9646279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.26036987565084,"score_gpt":0.4503271222721118,"score_spread":0.1899572466212718,"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."}}