{"id":"W4205536672","doi":"10.1109/access.2021.3124477","title":"Synthetic Benchmarks for Power Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Computer science; Cluster analysis; Benchmark (surveying); Data mining; Heuristic; Grid; Electric power system; Machine learning; Artificial intelligence; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001881515,0.001239567,0.000572363,0.002416535,0.0006447693,0.001138513,0.001860508,0.0009267216,0.006371305],"category_scores_gemma":[0.01056393,0.0002788268,0.0007286341,0.004445173,0.0005481513,0.001302482,0.0009986769,0.001384265,0.001426372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382797,"about_ca_system_score_gemma":0.0007589289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006121821,"about_ca_topic_score_gemma":0.01096627,"domain_scores_codex":[0.998006,0.0009139096,0.0001575141,0.0002359835,0.0005266904,0.000159891],"domain_scores_gemma":[0.9943405,0.002998243,0.0003989455,0.001047925,0.000997087,0.0002171816],"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":[0.0004398863,0.0006062815,0.009651314,0.001278613,0.0002722225,0.0004598665,0.0001613834,0.6895269,0.001683981,0.02771063,0.194525,0.073684],"study_design_scores_gemma":[0.0002338216,0.000315535,0.00650499,0.0002324728,0.00005765932,0.0004807073,0.0002959076,0.8402444,0.004447001,0.04712034,0.1000106,0.00005663644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3263651,0.006614016,0.2673024,0.003642687,0.001851113,0.002584331,0.2982797,0.0177471,0.07561361],"genre_scores_gemma":[0.6119116,0.001808364,0.09265687,0.0005907102,0.0001779005,0.00189456,0.2838421,0.00105067,0.006067291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006371305,"threshold_uncertainty_score":0.0213142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534347809276949,"score_gpt":0.2624484413973967,"score_spread":0.2471049633046272,"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."}}