{"id":"W2998971297","doi":"10.1155/2020/5081315","title":"Improving Synchronization in an Air and High-Speed Rail Integration Service via Adjusting a Rail Timetable: A Real-World Case Study in China","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Synchronization (alternating current); Subnetwork; Transport engineering; Service (business); Transfer (computing); Transfer station; Focus (optics); Rail network; China; Operations research; Computer science; Engineering; Computer network; Business","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":[0.001614928,0.001070495,0.0006979802,0.0006782089,0.001127061,0.0009267892,0.00154458,0.001587997,0.002006291],"category_scores_gemma":[0.002128265,0.0003598575,0.0008467831,0.001409239,0.00101665,0.001353882,0.0008549947,0.0006930884,0.0001202346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004508671,"about_ca_system_score_gemma":0.002428864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1305441,"about_ca_topic_score_gemma":0.1076244,"domain_scores_codex":[0.9992033,0.0002839139,0.00002901066,0.0001130584,0.0001074922,0.000263278],"domain_scores_gemma":[0.9985958,0.0006724556,0.0001977293,0.000092383,0.000224512,0.0002171466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002006274,0.0002951653,0.01108772,0.00005991423,0.00005371374,0.001705597,0.0001693502,0.976357,0.001668452,0.00202823,0.0007360962,0.005638057],"study_design_scores_gemma":[0.00007737518,0.0002343401,0.007283153,0.000006561134,0.0000467818,0.00009495886,0.0008101193,0.9890891,0.0009398091,0.0008026058,0.0005926349,0.000022556],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923814,0.00005767712,0.005352903,0.0001414119,0.000009533952,0.00005823619,0.0001108962,0.00004615072,0.001841734],"genre_scores_gemma":[0.9963548,0.00004575573,0.002708312,0.00001013268,0.00000333782,0.00002052333,0.000101252,0.000008811883,0.0007470177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1305441,"threshold_uncertainty_score":0.2595686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008266838730882303,"score_gpt":0.2261389514664262,"score_spread":0.2178721127355439,"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."}}