{"id":"W2792331020","doi":"10.1109/tpwrs.2018.2809548","title":"Power System Coherency Identification Under High Depth of Penetration of Wind Power","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electric power system; Salient; Wind power; Computer science; Control theory (sociology); Power (physics); Wind speed; Power system simulation; Engineering; Electrical engineering; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005152235,0.0002837728,0.0004923395,0.0003200235,0.0001074942,0.00005360285,0.0002442315,0.0002478388,0.000322744],"category_scores_gemma":[0.000006103891,0.0002839502,0.0001590383,0.0005434678,0.000120119,0.0002996269,0.000001329324,0.0001569934,0.0001181962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002317666,"about_ca_system_score_gemma":0.00006100639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001144376,"about_ca_topic_score_gemma":0.00006706705,"domain_scores_codex":[0.9974786,0.0001864042,0.001209381,0.0003469442,0.0005133287,0.0002653218],"domain_scores_gemma":[0.9982308,0.00007677786,0.0002857232,0.0007850985,0.0005086053,0.000113048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003434857,0.00119269,0.0008525662,0.002780165,0.001260267,0.000005380843,0.008403671,0.8448119,0.1247284,0.01336302,0.002084917,0.0001736218],"study_design_scores_gemma":[0.00887144,0.004174891,0.06723955,0.004140905,0.0007726991,0.0002514584,0.02336137,0.2438702,0.6375915,0.0001346755,0.005071521,0.004519746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1206054,0.000120601,0.8597506,0.000009767535,0.00623581,0.0007002082,0.0001462612,0.0002871487,0.01214418],"genre_scores_gemma":[0.9993461,0.000005778315,0.0001150807,0.00000535018,0.000010025,0.00004241255,0.000008643634,0.00005634764,0.0004102429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8787407,"threshold_uncertainty_score":0.9999613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095656186536516,"score_gpt":0.2192720529174762,"score_spread":0.2083154910521111,"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."}}