{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000739329,0.0001026753,0.000130851,0.00002812705,0.00003621162,0.0001671013,0.0001756816,0.00007428847,0.0001357447],"category_scores_gemma":[0.00003792149,0.0001104891,0.00005794915,0.000133172,0.00001091965,0.0002366369,0.00001980655,0.00007063936,0.00004497716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006813487,"about_ca_system_score_gemma":0.00001802726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004766599,"about_ca_topic_score_gemma":0.000003532684,"domain_scores_codex":[0.9993647,0.00001044631,0.0001523195,0.0001560642,0.0000972351,0.0002192436],"domain_scores_gemma":[0.9995453,0.00005554739,0.00001618185,0.0002376617,0.00009169184,0.00005364144],"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":[0.00004993003,0.0002205144,0.001906359,0.001777626,0.000434003,0.0001516181,0.0002705402,0.4826305,0.06391731,0.007309717,0.4381167,0.003215211],"study_design_scores_gemma":[0.001773675,0.0001369324,0.003664722,0.0004914451,0.0001884497,0.0001389861,0.0002504641,0.3394764,0.3248329,0.0009421936,0.3264053,0.001698629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5676024,0.002508773,0.3781847,0.0001557452,0.01227351,0.0006844109,0.0004602496,0.0005985039,0.03753166],"genre_scores_gemma":[0.9994628,0.00001429706,0.0001189475,0.00001964601,0.00007580625,0.00006897845,0.00008954335,0.00002438821,0.0001255352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4318604,"threshold_uncertainty_score":0.4505617,"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."}}