{"id":"W4388693236","doi":"10.1371/journal.pone.0290723","title":"Hazardous materials facility siting optimization and ranking: A transportation risk mitigation framework","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Hazardous waste; Flexibility (engineering); Ranking (information retrieval); Computer science; Rank (graph theory); Context (archaeology); Facility location problem; Flow network; Routing (electronic design automation); Scheduling (production processes); Network planning and design; Operations research; Risk analysis (engineering); Population; Mathematical optimization; Engineering; Business; Mathematics; Geography; Statistics; Artificial intelligence; Waste management","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":[0.002279164,0.00009888014,0.0002975871,0.0002149343,0.0002979362,0.0002276687,0.0001387271,0.0001095221,0.0004388268],"category_scores_gemma":[0.00269449,0.00008629281,0.00005643576,0.0010838,0.0000513674,0.0003123587,0.00001394638,0.00009920266,0.000191214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001444993,"about_ca_system_score_gemma":0.00001695654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005732228,"about_ca_topic_score_gemma":0.00002653838,"domain_scores_codex":[0.9976555,0.0002688571,0.0005685925,0.000380036,0.0009631442,0.000163943],"domain_scores_gemma":[0.9984663,0.0006973589,0.0002771575,0.0002658599,0.0002293474,0.00006397847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004478439,0.0007002346,0.4662676,0.0001934482,0.000881812,0.00003790935,0.02132236,0.3388754,0.06220388,0.001837741,0.0003331525,0.1068987],"study_design_scores_gemma":[0.0009795509,0.0001159226,0.3847183,0.0002651534,0.0008223631,8.611775e-7,0.002569328,0.3431329,0.05811392,0.2085738,0.00007432517,0.0006336611],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9259377,0.00003418138,0.07248762,0.0009710362,0.00003860535,0.0001569872,0.0002066898,0.0001116348,0.00005561779],"genre_scores_gemma":[0.9832473,0.0004956061,0.01584492,0.00003560589,0.00005497178,0.00001603317,0.0001610421,0.000006389868,0.0001380644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.206736,"threshold_uncertainty_score":0.4804846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0924315671378161,"score_gpt":0.3088011453317744,"score_spread":0.2163695781939583,"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."}}