{"id":"W2904533698","doi":"10.1155/2018/9789316","title":"Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Regret; Maximization; Minification; Computer science; Schedule; Public transport; Mathematical optimization; Context (archaeology); Operations research; Utility maximization; Transport engineering; Engineering; Mathematics; Mathematical economics; Machine learning; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002315254,0.0008088471,0.001161258,0.000637142,0.0004514192,0.001062498,0.001285838,0.0007268324,0.001296649],"category_scores_gemma":[0.004248856,0.0005111344,0.00121005,0.0007772797,0.0008853834,0.001490217,0.0008873217,0.001091171,0.000129481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001652754,"about_ca_system_score_gemma":0.001136147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01254009,"about_ca_topic_score_gemma":0.0064228,"domain_scores_codex":[0.9981375,0.0009668021,0.0000574542,0.0003606582,0.0002403989,0.0002372043],"domain_scores_gemma":[0.9982606,0.001052189,0.0002441614,0.0001080392,0.0002400769,0.00009496061],"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":[0.00002753058,0.00002051039,0.0007393472,0.00002217362,0.00003205501,0.0000449375,0.0000289925,0.9853416,0.0001885408,0.008800836,0.0001634744,0.00459006],"study_design_scores_gemma":[0.000002506735,0.00001153019,0.0001488727,0.000001498711,0.00000663491,0.000007832386,0.000005678046,0.9966724,0.00006041077,0.002998372,0.00008105948,0.000003216935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08357155,0.0004104316,0.9128367,0.0003059572,0.00003179676,0.00004815139,0.00006863137,0.0001470644,0.002579667],"genre_scores_gemma":[0.9474047,0.0002464863,0.04977066,0.00006111381,0.00003960458,0.00007786198,0.00009477551,0.00003535869,0.002269354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01254009,"threshold_uncertainty_score":0.02493423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494958934194427,"score_gpt":0.2699042215849947,"score_spread":0.2549546322430504,"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."}}