{"id":"W2913951305","doi":"10.1145/3308897.3308936","title":"Leveraging Sparsity in Distribution Grids","year":2019,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Admittance; Computer science; Sparse matrix; Sparse grid; Distribution (mathematics); Power (physics); Grid; Harmonics; Power grid; Matrix (chemical analysis); Algorithm; Mathematical optimization; Mathematics; Physics; Engineering; Electrical engineering; Mathematical analysis; Materials science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026124,0.0001887171,0.0003128863,0.0002030145,0.0000427059,0.00002972537,0.0003115217,0.0000817546,0.000489391],"category_scores_gemma":[0.000894135,0.000200186,0.00007034247,0.002372321,0.00001233197,0.0005553209,0.00007312539,0.0002694834,0.001028205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007722419,"about_ca_system_score_gemma":0.00005548497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004165551,"about_ca_topic_score_gemma":0.000001241268,"domain_scores_codex":[0.9981936,0.00007752629,0.0004846305,0.000252018,0.0006783051,0.000313904],"domain_scores_gemma":[0.9989591,0.00009053042,0.0000819767,0.0005889535,0.0002215962,0.00005787256],"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":[0.00001304015,0.0001021508,0.3127109,0.006589032,0.00004406825,0.000001905844,0.00007855003,0.1161848,0.0003602138,0.000140844,0.01457762,0.549197],"study_design_scores_gemma":[0.0008534328,0.00007962348,0.3719279,0.002207783,0.00009491876,0.000006475127,0.00001124172,0.5973259,0.0006105204,0.00004762453,0.02634317,0.0004914051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9564168,0.03827895,0.001472522,0.0001417057,0.0007709454,0.001195811,0.00002413208,0.0001541029,0.001544986],"genre_scores_gemma":[0.9595926,0.03919752,0.0002295395,0.0001053666,0.00003845811,0.00006198754,0.0007402569,0.00001710764,0.00001709995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5487055,"threshold_uncertainty_score":0.9997496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03397487497074961,"score_gpt":0.2768195274758123,"score_spread":0.2428446525050627,"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."}}