{"id":"W4312671043","doi":"10.1109/tpwrs.2022.3212925","title":"Fast Optimal Power Flow With Guarantees via an Unsupervised Generative Model","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Generative model; Mathematical optimization; Grid; Range (aeronautics); Granularity; Power flow; Electric power system; Generative grammar; Data mining; Power (physics); Artificial intelligence; Engineering; Mathematics","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":[0.0001854886,0.0004164821,0.0003650128,0.0002246884,0.0004716181,0.0001159851,0.0003049103,0.0001068359,0.0003690619],"category_scores_gemma":[6.075417e-7,0.0004156824,0.0001315267,0.0004304147,0.00005408407,0.0004820258,0.000002589395,0.0005637185,0.00006893815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004363516,"about_ca_system_score_gemma":0.00006212997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000416504,"about_ca_topic_score_gemma":0.00002023395,"domain_scores_codex":[0.9979293,0.000122153,0.0003988394,0.000472633,0.0005843504,0.0004927439],"domain_scores_gemma":[0.9990769,0.00002370347,0.00004643749,0.0005679768,0.0001052945,0.0001796751],"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":[0.0001283104,0.0002353403,0.000002764004,0.00002282802,0.0001616998,0.00002299117,0.001748857,0.9879537,0.009051138,0.00004314518,0.0005072352,0.0001220461],"study_design_scores_gemma":[0.0008391427,0.0007977463,0.000009198467,0.00002361906,0.00005241671,0.0001078659,0.001274264,0.9905385,0.005215306,0.000002803366,0.0006214646,0.0005176141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286393,0.0001181,0.8662962,0.00002882552,0.001770072,0.0005476047,0.001150902,0.0006516657,0.0007973262],"genre_scores_gemma":[0.9967923,0.000004924464,0.002154821,0.00004265265,0.00001740787,0.0004609196,0.00007437471,0.0001229194,0.0003297497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8681529,"threshold_uncertainty_score":0.9998295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009250292995344899,"score_gpt":0.1997346546662892,"score_spread":0.1904843616709443,"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."}}