{"id":"W7104024471","doi":"10.1109/tvt.2025.3629100","title":"Handoff Decision Optimization in Train Autonomous Control Systems Using a Rolling Prediction-Decision Framework","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Handover; Channel (broadcasting); Reliability (semiconductor); Control (management); Path (computing); Scheme (mathematics); Decision model; State (computer science)","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":[0.0009400519,0.0008655854,0.001007861,0.0003114497,0.0005028033,0.0009190119,0.0009911809,0.0009288188,0.001414372],"category_scores_gemma":[0.001674112,0.0004826293,0.0004874239,0.000363554,0.0007502349,0.0007878833,0.0009218028,0.001549375,0.0001240723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001248976,"about_ca_system_score_gemma":0.001986289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03281595,"about_ca_topic_score_gemma":0.01772296,"domain_scores_codex":[0.9994881,0.0000960859,0.00002540285,0.000143069,0.0001071264,0.0001401975],"domain_scores_gemma":[0.9992442,0.000417589,0.0001012196,0.00002556383,0.0001546485,0.00005674527],"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":[0.00002683185,0.00001573994,0.0002418578,0.00001311759,0.000009444214,0.00002476415,0.00001779936,0.9911419,0.000325459,0.001318089,0.0001593087,0.00670555],"study_design_scores_gemma":[0.000001498746,0.000004855456,0.00002603837,6.454454e-7,0.000001612201,0.000001018953,0.000001305142,0.99962,0.00004207394,0.0002789568,0.00002117741,9.489216e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07826249,0.0007316345,0.9157394,0.0006570838,0.00009854841,0.00005348111,0.00009621167,0.0004137774,0.00394733],"genre_scores_gemma":[0.9838665,0.0001494399,0.01409951,0.000104427,0.00002947543,0.0000435781,0.00006060921,0.00001673079,0.001629823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03281595,"threshold_uncertainty_score":0.06524992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006633378201355161,"score_gpt":0.2260439211662787,"score_spread":0.2194105429649236,"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."}}