{"id":"W4385480031","doi":"10.1155/2023/3448864","title":"Prediction and Impact Analysis of Passenger Flow in Urban Rail Transit in the Postpandemic Era","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China; Jinling Institute of Technology","keywords":"Urban rail transit; Autoregressive model; Flow (mathematics); Artificial neural network; Public transport; Time series; Transport engineering; Computer science; Simulation; Statistics; Meteorology; Environmental science; Engineering; Geography; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003132822,0.00006730547,0.0001773396,0.000782946,0.000009937879,0.000005633314,0.00005197563,0.00003885523,0.000003409149],"category_scores_gemma":[0.000005067541,0.00005222361,0.00008805938,0.001098298,0.00001057487,0.0002718461,3.82588e-7,0.0001598611,9.885824e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002997963,"about_ca_system_score_gemma":0.00000672561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006446938,"about_ca_topic_score_gemma":0.0002381603,"domain_scores_codex":[0.9992925,0.00002037809,0.0003977757,0.00005416563,0.000153726,0.00008144863],"domain_scores_gemma":[0.9997921,0.0000376278,0.00007036566,0.00005356429,0.00002682721,0.0000195072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005313212,0.00002081826,0.0264298,0.00003496531,0.0001487978,0.00001447184,0.005066858,0.9537063,0.004822558,0.00003013446,0.0001997241,0.009472446],"study_design_scores_gemma":[0.0005251687,0.00006971874,0.9393058,0.00004327398,0.0001732031,0.000001262515,0.0006305786,0.05892397,0.0001491826,0.0000522119,0.00008601154,0.00003964917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845991,0.0001561491,0.01482936,0.00007714471,0.0000713419,0.0001029563,0.00002488445,0.0001139852,0.00002513464],"genre_scores_gemma":[0.9982583,0.001278241,0.0003865726,0.000009259406,0.00001417842,0.000004623542,0.0000410367,0.000006677829,0.000001127488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.912876,"threshold_uncertainty_score":0.2129617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007596850909240136,"score_gpt":0.2360609690354064,"score_spread":0.2284641181261663,"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."}}