{"id":"W4401283857","doi":"10.1016/j.tre.2024.103679","title":"A method of time-varying demand distribution estimation for high-speed railway networks with user equilibrium model","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Estimation; Distribution (mathematics); Travel time; Computer science; Transport engineering; Econometrics; Economics; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00142524,0.0008935669,0.001594969,0.0008761149,0.0006670096,0.0009779815,0.002089379,0.001412704,0.0027481],"category_scores_gemma":[0.003711327,0.0007915727,0.001507179,0.001190586,0.0005041333,0.00177896,0.001017507,0.001679102,0.0006830625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007926872,"about_ca_system_score_gemma":0.001721007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01427522,"about_ca_topic_score_gemma":0.008688458,"domain_scores_codex":[0.9991374,0.0002933381,0.00004405103,0.0002690348,0.0001795907,0.00007648634],"domain_scores_gemma":[0.9984291,0.00098308,0.00007001883,0.00008022838,0.0003980772,0.00003942494],"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":[0.0001366425,0.0001156756,0.001829714,0.0002511833,0.0001766925,0.0001583064,0.0001398546,0.779243,0.005426978,0.02257209,0.002908984,0.1870408],"study_design_scores_gemma":[0.000004114801,0.000009628453,0.0001136539,0.000003149493,0.000009734391,0.00001647313,0.000006139356,0.9978803,0.0002466856,0.001419194,0.0002835794,0.000007338596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002105629,0.00008798198,0.9973127,0.00002611559,0.00001574669,0.0000157152,0.00001975731,0.00009949337,0.0003167664],"genre_scores_gemma":[0.3682866,0.0009603028,0.6209908,0.0001620727,0.0001560285,0.0004553182,0.0006173008,0.0001946151,0.008177077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01427522,"threshold_uncertainty_score":0.02838427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0803540560281141,"score_gpt":0.4045654710399509,"score_spread":0.3242114150118368,"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."}}