{"id":"W4412076628","doi":"10.1016/j.cie.2025.111343","title":"Managing emergency logistics for hazardous materials with random severity level and link disruption: A distributionally robust optimization approach","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Hazardous waste; Robust optimization; Link (geometry); Computer science; Operations research; Mathematical optimization; Engineering; Waste management; Mathematics; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001854821,0.001186985,0.001085919,0.000856537,0.0004309554,0.001598854,0.001076367,0.001387118,0.001863068],"category_scores_gemma":[0.003010701,0.0007718601,0.001234499,0.0006576292,0.0009154227,0.001512459,0.001669258,0.001487605,0.0001852688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193249,"about_ca_system_score_gemma":0.001522489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003833887,"about_ca_topic_score_gemma":0.002096054,"domain_scores_codex":[0.9991305,0.00037796,0.00003220939,0.0001652031,0.0001596243,0.0001345172],"domain_scores_gemma":[0.9988357,0.0006647524,0.0002153836,0.00006030143,0.0001545365,0.00006921226],"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":[0.00001109169,0.0000132038,0.0001045849,0.00001349263,0.0000135276,0.00002322792,0.000009450356,0.9930955,0.0003693539,0.004091099,0.0000992689,0.002156229],"study_design_scores_gemma":[0.000003164969,0.0000170981,0.00004294691,0.000002449048,0.000004048899,0.000006091998,0.00000840767,0.9969025,0.0001247616,0.002785246,0.00009999034,0.000003251716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01526397,0.0001422704,0.9814551,0.000316936,0.00001970826,0.00004734496,0.00004789245,0.00007105639,0.002635677],"genre_scores_gemma":[0.8311374,0.0004873028,0.1638696,0.0001904868,0.00006590002,0.0001996215,0.0001458146,0.0000826909,0.003821286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003833887,"threshold_uncertainty_score":0.009809375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1060863917845149,"score_gpt":0.3020008639622632,"score_spread":0.1959144721777483,"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."}}