{"id":"W4283828954","doi":"10.1155/2022/4672617","title":"Rail Transit Prediction Based on Multi-View Graph Attention Networks","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Municipal Education Commission","keywords":"Computer science; Graph; Autoencoder; Beijing; Data mining; Artificial intelligence; Artificial neural network; Intelligent transportation system; Machine learning; Smoothing; Centrality; Theoretical computer science; Engineering; Transport engineering","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":[0.0003134673,0.0007666105,0.0005970183,0.0009903829,0.0002865198,0.0004851776,0.0009480597,0.0007318914,0.00108782],"category_scores_gemma":[0.001166196,0.0003733448,0.0006791879,0.0007336074,0.0002656271,0.001201081,0.0005878309,0.0009669716,0.0001984996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128322,"about_ca_system_score_gemma":0.0006162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04926052,"about_ca_topic_score_gemma":0.04808955,"domain_scores_codex":[0.9997619,0.00003471267,0.000007848667,0.0001035969,0.00004081584,0.00005123873],"domain_scores_gemma":[0.9996308,0.0001540809,0.00004298435,0.00002974133,0.0001152061,0.0000270881],"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":[0.0001855845,0.000118527,0.006014104,0.00003656111,0.000075761,0.0001298298,0.00005390715,0.8942167,0.002569683,0.002255146,0.003069131,0.09127518],"study_design_scores_gemma":[0.000001267306,0.000004943649,0.0002678348,7.918186e-7,0.000003852183,0.000003390389,0.00000214505,0.9992218,0.0001105157,0.0003398761,0.00004246882,0.000001147738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4337247,0.001370618,0.5544459,0.001137439,0.0002144095,0.00007064544,0.0008082436,0.002891211,0.005336891],"genre_scores_gemma":[0.9860561,0.0001555842,0.01173577,0.00009496227,0.00003447344,0.00001506512,0.0004885436,0.0000241666,0.001395353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04926052,"threshold_uncertainty_score":0.0979476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006995024786702892,"score_gpt":0.2098441874618146,"score_spread":0.2028491626751117,"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."}}