{"id":"W2115097216","doi":"10.1109/tpwrs.2010.2050344","title":"Neural-Network Security-Boundary Constrained Optimal Power Flow","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Electric power system; Artificial neural network; Context (archaeology); Mathematical optimization; Constraint (computer-aided design); Benchmark (surveying); Representation (politics); Differentiable function; Boundary (topology); Process (computing); Function (biology); Power (physics); Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133463,0.000841474,0.001096933,0.00048847,0.0003481043,0.001062406,0.001023981,0.001355801,0.001833058],"category_scores_gemma":[0.00271071,0.0005089049,0.0004871441,0.000539886,0.0009041387,0.001672475,0.0007794303,0.00123274,0.0002576121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102939,"about_ca_system_score_gemma":0.001014332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008128091,"about_ca_topic_score_gemma":0.00583881,"domain_scores_codex":[0.9994372,0.0002160164,0.00002458099,0.00009647822,0.0001727744,0.00005307631],"domain_scores_gemma":[0.9994005,0.0003604928,0.00007783099,0.00003572748,0.0001096018,0.00001584635],"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":[0.000008428642,0.000005884433,0.00005268183,0.000008360291,0.000004324763,0.000008679807,0.000006491464,0.9918086,0.0001352694,0.003701741,0.0001139671,0.004145605],"study_design_scores_gemma":[0.000001187744,0.000001966889,0.000009061343,0.000001160956,5.15557e-7,0.000001191714,6.355003e-7,0.9985771,0.00004047421,0.001302741,0.00006316754,8.163192e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007093902,0.0001061913,0.9896254,0.00009728582,0.00001937231,0.0000190154,0.00003381408,0.0001138064,0.002891105],"genre_scores_gemma":[0.8061695,0.0002188933,0.1884275,0.000104333,0.00005170566,0.0002336526,0.000161794,0.00008053662,0.004552084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008128091,"threshold_uncertainty_score":0.01616156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005808650260617126,"score_gpt":0.2028856245994786,"score_spread":0.1970769743388615,"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."}}