{"id":"W4415673418","doi":"10.1016/j.grets.2025.100303","title":"Secure and scalable power system event identification with renewable integration via federated LSTM and adaptive privacy mechanisms","year":2025,"lang":"en","type":"article","venue":"Green Technologies and Sustainability","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"Science and Technology Project of State Grid","keywords":"Scalability; Differential privacy; Event (particle physics); Artificial noise; Electric power system; Cyber-physical system; Grid; Identification (biology); Event data","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":[0.000242703,0.0001610356,0.0001935565,0.000118616,0.0002627828,0.00009910452,0.00009576405,0.0001999372,7.808098e-7],"category_scores_gemma":[0.00008538972,0.0001248132,0.00001416035,0.0003364226,0.0002082984,0.0002062621,0.0001324916,0.000177089,1.724145e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001891976,"about_ca_system_score_gemma":0.00003590376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003764734,"about_ca_topic_score_gemma":0.000292328,"domain_scores_codex":[0.9991482,0.00002792726,0.0001947908,0.0003284072,0.00009056221,0.0002100725],"domain_scores_gemma":[0.9994756,0.00003764431,0.00003685735,0.000243398,0.0001789346,0.00002755474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001402978,0.0005347024,0.04597324,0.0184346,0.000863184,0.0001852557,0.01197123,0.009427717,0.02925253,0.3804246,0.001535603,0.4999944],"study_design_scores_gemma":[0.001437826,0.0008410519,0.03746737,0.0006315901,0.0001383721,0.00009808733,0.2225676,0.4473834,0.07739631,0.2102646,0.0007649585,0.001008862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7338546,0.001296634,0.2625562,0.0006733935,0.00006042054,0.0006355527,0.000005336597,0.0008043897,0.0001135212],"genre_scores_gemma":[0.9991573,0.0001043565,0.0005371293,0.00000533975,0.000002734603,0.00006841282,0.000003450334,0.000007235345,0.0001140402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4989856,"threshold_uncertainty_score":0.5089733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002912677079626448,"score_gpt":0.1922942659185731,"score_spread":0.1893815888389467,"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."}}