{"id":"W4413359292","doi":"10.1109/tsg.2025.3600714","title":"A Hybrid Imitation–Reinforcement Learning Framework for Optimal Operation of Soft Open Points in Unbalanced Distribution Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Imitation; Computer science; Reinforcement; Artificial intelligence; Control theory (sociology); Mathematical optimization; Engineering; Mathematics; Structural engineering; Control (management); Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002908432,0.0001897356,0.0002544228,0.0001822052,0.0001406769,0.00007258689,0.0002266554,0.00008854472,0.00004318116],"category_scores_gemma":[0.00002129245,0.0002194318,0.00008860088,0.0003405821,0.0000255498,0.0002734248,0.000005550653,0.0002840035,0.000006984037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002680059,"about_ca_system_score_gemma":0.00003082956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004367827,"about_ca_topic_score_gemma":0.00004613676,"domain_scores_codex":[0.9988372,0.0000400968,0.0004618001,0.0002435012,0.000140853,0.0002765056],"domain_scores_gemma":[0.9994382,0.0001677116,0.00005303011,0.0002337602,0.00006636765,0.00004097845],"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":[0.0001282262,0.00006117642,0.00006441135,0.00006028195,0.0000930785,7.666785e-7,0.00007327281,0.9928017,0.00009435634,0.001447667,0.0007796488,0.004395423],"study_design_scores_gemma":[0.001064897,0.0001091729,0.000619141,0.000200064,0.00004553053,5.276314e-7,0.00007723294,0.9842466,0.01027033,0.0001799774,0.002994459,0.0001920394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0140537,0.00002473947,0.9824342,0.0001746704,0.002020126,0.0008033334,0.00003134507,0.000127602,0.0003302813],"genre_scores_gemma":[0.9929101,0.00005679763,0.005854448,0.00007906225,0.00006688034,0.0006217588,0.0001854813,0.00002663088,0.0001988743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9788564,"threshold_uncertainty_score":0.8948169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115081111815816,"score_gpt":0.2508123372783509,"score_spread":0.2396615261601928,"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."}}