{"id":"W4413677388","doi":"10.1109/tsg.2025.3602849","title":"Safe Deep Reinforcement Learning for Resilient Self-Proactive Distribution Grids Against Wildfires","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Reinforcement; Computer science; Distribution (mathematics); Artificial intelligence; Engineering; Mathematics; Structural engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009274764,0.000862075,0.0007793553,0.0002451258,0.000339384,0.0006507228,0.0009630866,0.0008089726,0.001813295],"category_scores_gemma":[0.002329208,0.0004324,0.0004283344,0.0002020637,0.0007016988,0.0006505263,0.001073795,0.001754808,0.0002417246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009943643,"about_ca_system_score_gemma":0.001586831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009422723,"about_ca_topic_score_gemma":0.009130558,"domain_scores_codex":[0.9997302,0.00006635217,0.00001310991,0.00006634325,0.00005328558,0.00007071334],"domain_scores_gemma":[0.9991739,0.0004630825,0.0001071351,0.00004892463,0.0001390452,0.00006789539],"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":[0.00004137373,0.00004375817,0.0005609716,0.00002715646,0.00001906397,0.00004859448,0.00002887479,0.9781929,0.0007049688,0.001700551,0.0005254251,0.01810629],"study_design_scores_gemma":[0.000003507085,0.000009295133,0.00002974979,0.000001692482,0.000001718512,0.000001908458,0.000001955057,0.9989801,0.00009841189,0.0008115254,0.00005900157,0.000001058892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1058093,0.0007609708,0.8857915,0.0007826596,0.000108452,0.00006369829,0.0001052873,0.001878932,0.00469919],"genre_scores_gemma":[0.9750968,0.0001075446,0.02303382,0.0001463891,0.00001753557,0.00005586015,0.00008342496,0.00003869238,0.001419871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009422723,"threshold_uncertainty_score":0.01873577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006135842061137021,"score_gpt":0.2204674610041962,"score_spread":0.2143316189430592,"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."}}