{"id":"W2148714761","doi":"10.1002/atr.193","title":"Railway passenger train delay prediction via neural network model","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Artificial neural network; Test set; Artificial intelligence; Machine learning; Set (abstract data type); Train; Data mining; Test data; Data set; Time delay neural network","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":[0.000411478,0.0005192893,0.0003916746,0.0005320023,0.0002399242,0.0005460518,0.0005660542,0.0005565332,0.001401721],"category_scores_gemma":[0.001326315,0.0001928475,0.0004730816,0.0005960092,0.0001737216,0.0005899626,0.0002626269,0.0006162249,0.0002902859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009518579,"about_ca_system_score_gemma":0.0006786757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03791415,"about_ca_topic_score_gemma":0.02049306,"domain_scores_codex":[0.9998285,0.000039559,0.00001052755,0.00005307486,0.00003922039,0.00002917541],"domain_scores_gemma":[0.9996555,0.000144453,0.0000364399,0.00001472356,0.0001375109,0.00001140414],"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":[0.00003787989,0.00002019313,0.001805385,0.00001401739,0.00001625692,0.00001816048,0.000009272584,0.987,0.0003246925,0.000492532,0.0003505257,0.009911053],"study_design_scores_gemma":[9.331224e-7,0.000003293223,0.0001631531,9.693338e-7,0.000002114943,0.000001273784,0.000001133768,0.9995844,0.0000766782,0.0001243148,0.00004051138,0.000001131701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4915394,0.0007696115,0.4930341,0.0007533347,0.0001781404,0.00008439923,0.000836932,0.001437586,0.01136644],"genre_scores_gemma":[0.9817473,0.0001486877,0.01477778,0.00002721205,0.00002500507,0.000049509,0.0002849916,0.00001710941,0.002922352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03791415,"threshold_uncertainty_score":0.07538694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00773148274247261,"score_gpt":0.2049145313019972,"score_spread":0.1971830485595246,"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."}}