{"id":"W4415748289","doi":"10.1109/tsg.2025.3627870","title":"Optimal Scheduling of Railway Power Supply Systems Integrated With Microgrids: A CCAH-RL Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; China Scholarship Council; Strategic Innovation Fund","keywords":"Electrification; Electricity; Electric power system; Energy supply; Renewable energy; Scheduling (production processes); Traction power network; Energy management; Demand response","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0007138222,0.0007480155,0.0008944588,0.0003117289,0.0003177641,0.0007226487,0.001003186,0.0006548333,0.002620696],"category_scores_gemma":[0.001627334,0.0004338213,0.0004200287,0.0003092631,0.0005098503,0.0005141221,0.0008180918,0.0008618779,0.0002436058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008457111,"about_ca_system_score_gemma":0.001289298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01362842,"about_ca_topic_score_gemma":0.01027439,"domain_scores_codex":[0.9997079,0.0001135048,0.00001137617,0.00005713763,0.00004905915,0.00006100574],"domain_scores_gemma":[0.9993128,0.0003797853,0.00009899333,0.00003781481,0.0001046873,0.00006598561],"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":[0.00002086573,0.00001525548,0.0001661905,0.00001739691,0.00001081518,0.00002348281,0.00001238522,0.9911954,0.0001859168,0.0013691,0.0002412136,0.006741934],"study_design_scores_gemma":[0.000004537311,0.000006263275,0.00002298193,0.000001064123,0.000001755645,0.000001553759,0.000003390895,0.9994308,0.0000304007,0.0004215156,0.00007472302,8.177537e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05695475,0.0004788538,0.9313236,0.000516908,0.0000820112,0.0001164899,0.00007906731,0.0005148863,0.009933397],"genre_scores_gemma":[0.941236,0.0001487773,0.05535725,0.00009917171,0.00004204624,0.00009445017,0.00007063326,0.00005654542,0.002895023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01362842,"threshold_uncertainty_score":0.02709818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005638797800268296,"score_gpt":0.1895342076510597,"score_spread":0.1838954098507914,"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."}}