{"id":"W2156699996","doi":"10.1109/pes.2009.5275901","title":"Distribution system loss minimization using optimal DG mix","year":2009,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada); University of Waterloo","funders":"","keywords":"Renewable energy; Distributed generation; Mathematical optimization; Probabilistic logic; Probability density function; Wind power; Minification; Linear programming; Reduction (mathematics); Wind speed; Computer science; Integer programming; Rayleigh distribution; Engineering; Mathematics; Meteorology; Statistics; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00008467526,0.0001545807,0.0001391105,0.00003789369,0.00007026627,0.00006752958,0.00009492557,0.0001092835,0.00003417482],"category_scores_gemma":[0.00001356202,0.0001639931,0.00005381045,0.0002438062,0.00001471615,0.0002824862,0.00001082181,0.00008379621,0.00007348351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004494924,"about_ca_system_score_gemma":0.0000117485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006712461,"about_ca_topic_score_gemma":5.877693e-7,"domain_scores_codex":[0.9991431,0.00001509756,0.0002410404,0.0001587357,0.0001706068,0.0002713929],"domain_scores_gemma":[0.9996504,0.000008860451,0.00002717914,0.0001694537,0.0000668413,0.00007727664],"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":[0.00002408485,0.00004865586,0.0002761299,0.00009152704,0.00002732528,0.00001986355,0.00004578057,0.9731321,0.009822577,0.009222583,0.005055056,0.002234342],"study_design_scores_gemma":[0.0002786235,0.00004660206,0.002264794,0.00004486967,0.00002728835,0.00004009276,0.00007106737,0.9855595,0.01014012,0.00001137517,0.001276301,0.0002393779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3699037,0.00005982393,0.6270136,0.0000348769,0.0002858768,0.0001235956,0.00008417942,0.000741364,0.001753022],"genre_scores_gemma":[0.9944162,0.000004530792,0.004839603,0.00001166775,0.0001106669,0.000002445741,0.0005713996,0.00001497552,0.00002849633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6245126,"threshold_uncertainty_score":0.6687445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00807080463090328,"score_gpt":0.2143874217427106,"score_spread":0.2063166171118073,"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."}}