{"id":"W2031370685","doi":"10.1109/naps.2013.6666827","title":"Accelerated parallel WLS state estimation for large-scale power systems on GPU","year":2013,"lang":"en","type":"article","venue":"","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Massively parallel; State (computer science); Parallel computing; CUDA; Multi-core processor; Computation; Parallel processing; Scale (ratio); Supercomputer; Power (physics); Computational science; Algorithm","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":[0.0001637638,0.0001348792,0.0001744986,0.0000518961,0.00005348871,0.0001326733,0.00008196143,0.00006557132,0.0005159472],"category_scores_gemma":[0.00002216998,0.0001137394,0.00004207595,0.0001027161,0.000005272225,0.0002348475,0.000008035976,0.00005255992,0.0003691107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006387032,"about_ca_system_score_gemma":0.000009369208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002453551,"about_ca_topic_score_gemma":0.00001037247,"domain_scores_codex":[0.999159,0.00002551122,0.0002972078,0.0001572568,0.0001139506,0.0002470757],"domain_scores_gemma":[0.999486,0.00005118217,0.00002984088,0.0002256068,0.0001208256,0.000086586],"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":[0.000009031349,0.00004412196,0.0001964545,0.0001067048,0.00002433722,1.742958e-7,0.0003817405,0.9777765,0.0001511019,0.001032395,0.02015729,0.0001201033],"study_design_scores_gemma":[0.0004658543,0.00003477893,0.001252,0.0000160023,0.00000267849,6.430657e-7,0.0001284611,0.9929482,0.0002061256,0.00005851065,0.004737845,0.0001489281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03104882,0.00003488116,0.949927,0.00006618482,0.0005758372,0.001106979,0.00003948858,0.0005673463,0.01663342],"genre_scores_gemma":[0.9915698,0.000003556532,0.005603914,0.00005466949,0.000005334014,0.0002904249,0.00005172671,0.00002751178,0.002393134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9605209,"threshold_uncertainty_score":0.564926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508035420245793,"score_gpt":0.2345451347142807,"score_spread":0.2194647805118227,"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."}}