{"id":"W1504312776","doi":"10.1016/s0927-0507(06)14010-4","title":"Chapter 10 Traffic Equilibrium","year":2006,"lang":"en","type":"book-chapter","venue":"Handbooks in operations research and management science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Traffic network; Nash equilibrium; Computer science; Incentive; Network packet; Telecommunications network; Operations research; Mathematical economics; Transport engineering; Engineering; Computer network; Mathematics; Economics; Microeconomics","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":[0.002128455,0.0001451347,0.0001421283,0.001125423,0.001348817,0.0005943287,0.0004091591,0.0001116646,0.0005712413],"category_scores_gemma":[0.00003744304,0.0001491007,0.00002626333,0.0002671478,0.002145022,0.0004387822,0.00008199377,0.0002651531,0.00009165129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360975,"about_ca_system_score_gemma":0.000207769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003246501,"about_ca_topic_score_gemma":0.005434515,"domain_scores_codex":[0.9973937,0.00003497393,0.0002747536,0.000556637,0.001242263,0.0004976526],"domain_scores_gemma":[0.9992358,0.00005102926,0.00002681496,0.0002375594,0.000292625,0.0001561715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001273292,0.00002565327,0.00001780233,0.00002929159,0.000006357559,0.00002761925,0.001426805,0.01480339,0.00001417442,0.9755783,0.00262079,0.005437046],"study_design_scores_gemma":[0.0008473117,0.0002109669,0.0005533592,0.0007657973,0.00002471738,0.000001476656,0.001179257,0.01118511,0.00003406246,0.008619199,0.9758294,0.0007493631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008650025,0.0003126358,0.0004657824,0.0006209495,0.0001172818,0.0007856446,0.00001347251,0.00005943754,0.9967598],"genre_scores_gemma":[0.05371615,0.001748112,0.001353776,0.00004387158,0.00008681288,0.00005336938,0.00005225416,0.00001639065,0.9429293],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9732086,"threshold_uncertainty_score":0.9999513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07130149340000251,"score_gpt":0.3635797970469616,"score_spread":0.2922783036469591,"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."}}