{"id":"W2736372245","doi":"","title":"Modelling expected train passenger delays on large scale railway networks","year":2006,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Scale (ratio); Computer science; Transport engineering; Engineering; Geography","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":[0.001117755,0.001420654,0.001165624,0.0008653743,0.0006168348,0.001874465,0.001611606,0.002843827,0.003941826],"category_scores_gemma":[0.007268291,0.001566063,0.001208593,0.0009923904,0.001275389,0.001904412,0.0008403755,0.001887463,0.0002757848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003950278,"about_ca_system_score_gemma":0.00148168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07767347,"about_ca_topic_score_gemma":0.03738175,"domain_scores_codex":[0.9995067,0.0001493799,0.00002378954,0.0001356079,0.00005105301,0.0001334143],"domain_scores_gemma":[0.9941929,0.004720051,0.0003394521,0.0001129074,0.000310362,0.0003242568],"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":[0.00001463239,0.000007353838,0.0002439378,0.000004747823,0.000003810383,0.00001487437,0.000004876636,0.9992046,0.00005227421,0.0002612079,0.00003865178,0.000149015],"study_design_scores_gemma":[0.000004581063,0.000008981474,0.0001768235,0.000001336479,0.000003395049,0.000003068186,0.00001003015,0.9994658,0.00004164247,0.0002472937,0.0000341796,0.000002785934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9412494,0.0003574711,0.05067461,0.0005425007,0.0001053868,0.00006800116,0.0009732041,0.0002124661,0.00581695],"genre_scores_gemma":[0.9934308,0.0001385285,0.002292909,0.00001801708,0.00001578692,0.00003151711,0.0003163234,0.00005254558,0.003703551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07767347,"threshold_uncertainty_score":0.1544427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007184434365010746,"score_gpt":0.1659400593967509,"score_spread":0.1587556250317402,"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."}}