{"id":"W4377030597","doi":"10.1016/j.cie.2023.109302","title":"A novel Markov model for near-term railway delay prediction","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain; Interpretability; Computer science; Benchmark (surveying); Markov model; Train; Kernel (algebra); Variable-order Markov model; Markov process; Term (time); Algorithm; Mathematical optimization; Machine learning; Mathematics; Statistics","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":[0.001027004,0.0007318565,0.001747452,0.0006551972,0.0006086155,0.001079211,0.002498025,0.001316803,0.00356414],"category_scores_gemma":[0.002709376,0.0007155914,0.001025057,0.0009830568,0.0005019964,0.001410313,0.001133614,0.001614478,0.0008361394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183858,"about_ca_system_score_gemma":0.002091721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02715459,"about_ca_topic_score_gemma":0.02403011,"domain_scores_codex":[0.9994337,0.0001057903,0.00003282923,0.0001842176,0.000120949,0.0001224533],"domain_scores_gemma":[0.9984735,0.001017098,0.0001391481,0.00007799335,0.0002224548,0.00006983914],"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":[0.00008822708,0.00003477345,0.0006662281,0.00003966533,0.00003357632,0.00005834792,0.00002276591,0.9727016,0.0008211111,0.009466947,0.0009257435,0.01514104],"study_design_scores_gemma":[0.00000273387,0.000004515082,0.00004353488,0.000001566959,0.000004377508,0.000004641276,9.291444e-7,0.9984251,0.00005020152,0.001370735,0.00008891717,0.000002702486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02207721,0.0005022398,0.9743667,0.0002888062,0.0001284774,0.00003103153,0.0005229064,0.0004838388,0.001598827],"genre_scores_gemma":[0.908036,0.001130719,0.07873128,0.0002506551,0.0002547485,0.0002031077,0.001524748,0.0001283317,0.009740577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02715459,"threshold_uncertainty_score":0.05399305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03081535407908533,"score_gpt":0.2101299205139754,"score_spread":0.17931456643489,"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."}}