{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000257516,0.0001342657,0.0001984605,0.00007727791,0.00004996094,0.000009483304,0.00007068355,0.00007792925,0.00001184655],"category_scores_gemma":[0.000003904219,0.0001194871,0.0001180697,0.0001670301,0.00001171256,0.0008680632,4.916564e-7,0.0001942911,0.000001897658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005009324,"about_ca_system_score_gemma":0.0000111154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001467605,"about_ca_topic_score_gemma":0.00001161667,"domain_scores_codex":[0.998835,0.00001575619,0.000562947,0.00006548053,0.0002379494,0.0002828185],"domain_scores_gemma":[0.9995376,0.00002104744,0.0001526542,0.00007895138,0.00007835688,0.0001313928],"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":[0.00002589315,0.00002384223,0.0008602747,0.00002426344,0.00002637518,0.000004074222,0.001178435,0.9789929,0.01052312,0.0001211693,0.0001918829,0.00802781],"study_design_scores_gemma":[0.001357206,0.0001783512,0.1298737,0.0001187453,0.000120538,0.00008982274,0.0003007913,0.8616595,0.001192022,0.0003607359,0.004395214,0.0003533272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7361521,0.001023562,0.2607796,0.00001994594,0.001698657,0.0000616455,0.000006292909,0.00006075533,0.0001974536],"genre_scores_gemma":[0.9922375,0.0001062774,0.006783903,0.00001677982,0.0007744167,0.000005106332,0.00001525372,0.00003114253,0.00002961037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2560854,"threshold_uncertainty_score":0.4872544,"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."}}