{"id":"W2152032313","doi":"10.1109/tpwrs.2006.873131","title":"Investigating Distributed Generation Systems Performance Using Monte Carlo Simulation","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monte Carlo method; Randomness; Electric power system; Computer science; Distributed generation; Mathematical optimization; Power (physics); Control theory (sociology); 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":[0.0002068472,0.0003147779,0.0002859076,0.0001725079,0.0003027441,0.0002250345,0.000112366,0.0002060213,0.000007505324],"category_scores_gemma":[0.000003906172,0.0003490981,0.00009087571,0.0004402434,0.00003645114,0.0005628032,8.47951e-7,0.0002632596,0.00004769836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006459181,"about_ca_system_score_gemma":0.00003023567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005030346,"about_ca_topic_score_gemma":0.00002271349,"domain_scores_codex":[0.9982061,0.00007633994,0.0006462765,0.000300279,0.0003845283,0.000386519],"domain_scores_gemma":[0.9992523,0.00004562193,0.0001090434,0.000331527,0.0001626852,0.00009884685],"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":[0.00000381374,0.00002984948,0.00009205651,0.0001073952,0.000033469,0.000001590067,0.00005655425,0.9843909,0.01499831,0.00001775919,0.0002280195,0.00004025421],"study_design_scores_gemma":[0.0003198205,0.00005811914,0.0001766545,0.0001567574,0.00004486288,0.00001807788,0.00006941357,0.9926746,0.005567425,4.650331e-7,0.0005706655,0.0003431082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4561598,0.0001624955,0.5397888,0.000002859962,0.002818317,0.0003347904,0.0002402719,0.0003902025,0.0001023783],"genre_scores_gemma":[0.9994248,0.000005038606,0.0001520772,0.000003968287,0.0001249617,0.00007216725,0.00007387195,0.00006738208,0.00007575755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5432649,"threshold_uncertainty_score":0.9998961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02229225268095488,"score_gpt":0.2242768504076488,"score_spread":0.2019845977266939,"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."}}