{"id":"W2142283289","doi":"10.1109/ccece.1998.682765","title":"Probabilistic optimal power flow","year":2002,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Consejo Nacional de Ciencia y Tecnología","keywords":"Probabilistic logic; Monte Carlo method; Mathematical optimization; Moment (physics); Computer science; Random variable; Nonlinear system; Transformation (genetics); Flow (mathematics); Statistical model; Power (physics); Algorithm; Mathematics; Artificial intelligence; Statistics","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":[0.001884679,0.0009666787,0.001299569,0.001004701,0.0004956815,0.001351178,0.001293796,0.001002055,0.004912743],"category_scores_gemma":[0.004992378,0.0006069024,0.0009400679,0.00123114,0.001324916,0.002173491,0.001142871,0.001485311,0.0005492473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001287898,"about_ca_system_score_gemma":0.001362238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002075013,"about_ca_topic_score_gemma":0.001741681,"domain_scores_codex":[0.9986664,0.0004893409,0.00004831703,0.0002274214,0.000484965,0.0000834775],"domain_scores_gemma":[0.9987963,0.0007724995,0.0001286358,0.00008604415,0.0001790027,0.00003753617],"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":[0.00001927205,0.00001693432,0.0001487079,0.00005997347,0.00002343732,0.00002917509,0.00003177554,0.6873272,0.0002826354,0.2891849,0.002251564,0.02062442],"study_design_scores_gemma":[0.000008282663,0.00001247097,0.00005746117,0.000008054081,0.000006384253,0.00002465941,0.000004805859,0.8668201,0.00009771837,0.1292197,0.003733213,0.000007284956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001107648,0.0002539191,0.9933229,0.0002058479,0.00004189188,0.0000170564,0.00004958366,0.00004879875,0.004952325],"genre_scores_gemma":[0.3997091,0.002703871,0.5767825,0.0003709967,0.0007029027,0.0004051141,0.0004961231,0.000259571,0.01856983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004912743,"threshold_uncertainty_score":0.01643473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008902395164997627,"score_gpt":0.1808824933357297,"score_spread":0.1719800981707321,"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."}}