{"id":"W4220750544","doi":"10.3390/en15062288","title":"State Estimation Fusion for Linear Microgrids over an Unreliable Network","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Agencia Estatal de Investigación; Universidad Carlos III de Madrid; Ministerio de Ciencia e Innovación; Comunidad de Madrid","keywords":"Computer science; Network packet; Lossy compression; Kalman filter; Linear regression; Packet loss; Mean squared error; Perceptron; Random forest; Sensor fusion; Multilayer perceptron; Fusion; Data mining; Real-time computing; Artificial intelligence; Machine learning; Artificial neural network; Statistics; Mathematics; Computer network","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":[0.0001225743,0.00007539885,0.00007649742,0.00003223423,0.0002471959,0.00002177334,0.0001050412,0.00002121901,0.00008194274],"category_scores_gemma":[0.000007312702,0.0000771943,0.00003013143,0.0001314128,0.00001541598,0.0001270816,0.00004721121,0.00008473315,0.000003712919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003256747,"about_ca_system_score_gemma":0.0000112914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003098503,"about_ca_topic_score_gemma":0.00001504805,"domain_scores_codex":[0.9994838,0.00001560611,0.0001021714,0.0001057537,0.0001025616,0.0001901227],"domain_scores_gemma":[0.9997767,0.00004377085,0.00001500519,0.0001221515,0.00001171418,0.00003064033],"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":[0.00001367184,0.00001245335,0.00009144234,0.00002033833,0.00000574048,0.000001158446,0.0003254923,0.9827279,0.002387589,0.0003093047,0.01189823,0.002206706],"study_design_scores_gemma":[0.0001627897,0.00007299192,0.0004472448,0.000007095147,0.000004960477,0.000002102305,0.00007169245,0.8674949,0.004696812,0.0007018562,0.1262197,0.0001178439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884155,0.0008341096,0.008656115,0.00002320903,0.001501448,0.0001061872,0.00002047959,0.0002694544,0.000173484],"genre_scores_gemma":[0.9851925,0.0001632832,0.01341092,0.00009940451,0.0003350847,0.0001095971,0.0001281953,0.000033509,0.0005274565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.115233,"threshold_uncertainty_score":0.3147892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006554675157147589,"score_gpt":0.2208415645724579,"score_spread":0.2142868894153103,"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."}}