{"id":"W2801314480","doi":"10.1155/2018/5983250","title":"Defining Reserve Times for Metro Systems: An Analytical Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Headway; Flexibility (engineering); Robustness (evolution); Computer science; Energy consumption; Context (archaeology); Exploit; Operations research; Energy (signal processing); Transport engineering; Simulation; Engineering; Computer security","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":[0.0003312462,0.0001019574,0.0002376195,0.0001449961,0.00005423517,0.00002619905,0.0001075483,0.00005981086,0.000004823547],"category_scores_gemma":[0.0000218533,0.00008685527,0.00009120041,0.0001819156,0.00002294867,0.0004550429,4.597766e-7,0.00008745201,0.000001407922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004106445,"about_ca_system_score_gemma":0.00002075201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000779467,"about_ca_topic_score_gemma":0.00002385479,"domain_scores_codex":[0.9990041,0.00001460568,0.0005191844,0.00009472619,0.0001997283,0.0001677072],"domain_scores_gemma":[0.9993741,0.00003982971,0.0001373326,0.00009962955,0.0002596195,0.00008946979],"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":[0.00007189903,0.00003058758,0.0003603678,0.0001187206,0.00005243936,0.000003064435,0.0007141149,0.9893771,0.001131471,0.006577714,0.0001235032,0.001438989],"study_design_scores_gemma":[0.00324309,0.002397198,0.02837716,0.0004053811,0.0002897105,0.0000493405,0.0077871,0.9397093,0.003427751,0.0006506665,0.01302132,0.0006419685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7415319,0.0008455103,0.2555156,0.000009293208,0.0008114025,0.0001145602,0.00001391584,0.0000524615,0.001105306],"genre_scores_gemma":[0.9757671,0.00001586331,0.02378508,0.000003991709,0.0003351239,0.000007542881,0.00002268906,0.00002503616,0.00003753023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2342352,"threshold_uncertainty_score":0.3541855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01482684820899085,"score_gpt":0.2594946885040685,"score_spread":0.2446678402950776,"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."}}