{"id":"W2106944463","doi":"10.18757/ejtir.2003.3.1.4236","title":"Effects of Anticipatory Control with Multiple User Classes","year":2003,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Control (management); Computer science; Genetic algorithm; Focus (optics); Mathematical optimization; Operations research; Traffic generation model; Optimal control; Game theory; Computer network; Engineering; Artificial intelligence; Mathematics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.002074432,0.00007615951,0.0001840639,0.0001822207,0.000209455,0.00002211244,0.0001296534,0.00003358957,0.00004319244],"category_scores_gemma":[0.0001784402,0.00005530269,0.00004291502,0.0002627409,0.0003791066,0.0002008391,0.000001147166,0.0003280545,5.939491e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001253727,"about_ca_system_score_gemma":0.0002194817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001903765,"about_ca_topic_score_gemma":0.00003280675,"domain_scores_codex":[0.9980395,0.0007774914,0.000293479,0.00009371821,0.0005831283,0.0002126881],"domain_scores_gemma":[0.9988599,0.0002843757,0.0001526834,0.00005724034,0.0004888417,0.0001569779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004067891,0.00005932847,0.9789578,0.0001397376,0.00009347244,0.0002868856,0.01165716,0.002580737,0.001755958,0.002300687,0.000364427,0.001397061],"study_design_scores_gemma":[0.002102138,0.0003793825,0.9648413,0.0001615363,0.00004762327,0.00000947531,0.001565302,0.000008483301,0.0009417975,0.0000560297,0.0298012,0.00008569271],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984475,0.0004756559,0.01023981,0.00008601524,0.0000960974,0.0001466956,0.000004391612,0.000008349275,0.004468005],"genre_scores_gemma":[0.9971688,0.000321829,0.002323816,0.0000199337,0.00006558582,4.134818e-7,0.000001415384,0.00001329216,0.00008493521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02943678,"threshold_uncertainty_score":0.2255178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183322750396889,"score_gpt":0.3009110506277041,"score_spread":0.2825787755880152,"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."}}