{"id":"W2035893651","doi":"10.1287/trsc.35.4.345.10433","title":"A Bilevel Model for Toll Optimization on a Multicommodity Transportation Network","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université de Montréal","funders":"","keywords":"Toll; Bilevel optimization; Mathematical optimization; Flow network; Revenue; Set (abstract data type); Computer science; Mathematics; Operations research; Optimization problem; Economics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001256603,0.0001900922,0.0001990593,0.0002352931,0.001572729,0.0001296422,0.0003500879,0.0001292696,0.00009105042],"category_scores_gemma":[0.00009919085,0.0002031978,0.0001046476,0.001397838,0.0004250686,0.001068959,6.013414e-7,0.0001156446,0.000008552394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001153999,"about_ca_system_score_gemma":0.0005326755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002730173,"about_ca_topic_score_gemma":0.003640463,"domain_scores_codex":[0.9974248,0.0000434425,0.0005024177,0.0005521227,0.0009053814,0.0005718303],"domain_scores_gemma":[0.9985904,0.0001732968,0.0002285658,0.0002090782,0.0005583119,0.0002404174],"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":[0.0001492576,0.0000791216,0.00712135,0.000008830813,0.000004485377,0.000001640119,0.01114429,0.9549472,0.00003762374,0.02470089,0.0001811943,0.001624134],"study_design_scores_gemma":[0.001014532,0.00007227062,0.06224529,0.00004944994,0.00004784915,1.778844e-7,0.0007327331,0.9311576,0.00004344068,0.001935713,0.002357721,0.0003432127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09750892,0.00001264614,0.8984694,0.000839352,0.0003469333,0.0008552496,0.0001479306,0.0002880541,0.001531552],"genre_scores_gemma":[0.9062107,0.0000857717,0.091962,0.0004133514,0.0001265691,0.0001319951,0.0004815321,0.00002150403,0.000566526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8087018,"threshold_uncertainty_score":0.9997271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06522803791999959,"score_gpt":0.3404594592474697,"score_spread":0.2752314213274701,"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."}}