{"id":"W2017712805","doi":"10.1007/s11431-010-4244-5","title":"Multi-criterion system optimization model for urban multimodal traffic network","year":2011,"lang":"en","type":"article","venue":"Science in China. Series E, Technological sciences/Science in China. Series E, Technological Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Mathematical optimization; Optimization problem; Mode (computer interface); Programming paradigm; Energy consumption; Simple (philosophy); Bilevel optimization; Engineering; Algorithm; Mathematics","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":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["sts"],"category_scores_codex":[0.02045697,0.0009022043,0.001034568,0.002584091,0.01013204,0.00147152,0.00931764,0.001088248,0.00009740266],"category_scores_gemma":[0.005361277,0.0007011661,0.0002225396,0.03070605,0.07855078,0.00903814,0.0008466758,0.00118654,0.00001146363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187349,"about_ca_system_score_gemma":0.002242999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009496261,"about_ca_topic_score_gemma":0.003271498,"domain_scores_codex":[0.9865429,0.0003145733,0.00178787,0.003759986,0.003438483,0.004156212],"domain_scores_gemma":[0.9969543,0.0002977403,0.0008481592,0.0008800608,0.0004338805,0.0005858305],"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":[0.0001374707,0.0005762564,0.03873716,0.00004363507,0.00000282238,0.00003794368,0.00660696,0.7409429,0.0009038082,0.2033665,0.00002986331,0.008614725],"study_design_scores_gemma":[0.0007968603,0.001287545,0.04502018,0.0003665085,0.00001752936,0.00004621009,0.02312414,0.9120799,0.0008466859,0.01467432,0.000234217,0.001505865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8983573,0.0002484096,0.08112583,0.004051437,0.001448259,0.003204999,0.00005578991,0.003410092,0.008097883],"genre_scores_gemma":[0.7501982,0.0003012096,0.2487675,0.00007886719,0.00005453558,0.0003865021,0.000007015668,0.00002012437,0.0001861327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1886922,"threshold_uncertainty_score":0.9995651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04436926087847434,"score_gpt":0.3010676006735444,"score_spread":0.2566983397950701,"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."}}