{"id":"W2123890255","doi":"10.1109/tpwrd.2009.2038385","title":"A Clustering-Based Method for Quantifying the Effects of Large On-Grid PV Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Power Systems and Renewable Energy","field":"Energy","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Photovoltaic system; Cluster analysis; Computer science; Electric power system; Grid; Data mining; Grid-connected photovoltaic power system; Power (physics); Real-time computing; Maximum power point tracking; Engineering; Artificial intelligence; Voltage; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032316,0.0008771653,0.0005957834,0.002795262,0.0007261947,0.0006572849,0.001048368,0.0008646725,0.001012159],"category_scores_gemma":[0.003685297,0.0003007978,0.0006563062,0.002270475,0.0002987632,0.0008394159,0.0004282575,0.0006534474,0.0003423344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008262293,"about_ca_system_score_gemma":0.0008250095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009321604,"about_ca_topic_score_gemma":0.009763349,"domain_scores_codex":[0.9993027,0.00019865,0.00004288039,0.0001418072,0.0002675389,0.00004636378],"domain_scores_gemma":[0.9986339,0.0005373688,0.0001697017,0.0001596165,0.0004548561,0.00004449319],"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":[0.0002928917,0.0001754282,0.0055515,0.0002538233,0.0002646738,0.0001731734,0.0002628203,0.6091842,0.02433679,0.0100638,0.003601921,0.345839],"study_design_scores_gemma":[0.00001301703,0.00004671721,0.003211755,0.00001394771,0.00003158113,0.0001118132,0.00004467525,0.9868855,0.004744354,0.002592462,0.002255186,0.00004890835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01383088,0.0001352012,0.9840968,0.00003921767,0.00003221893,0.00009082399,0.0001961594,0.0005784634,0.001000328],"genre_scores_gemma":[0.2499748,0.0002258158,0.7472759,0.00003817225,0.00004148014,0.0002445253,0.0005713706,0.0001453854,0.001482582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009321604,"threshold_uncertainty_score":0.01853466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529010631107013,"score_gpt":0.2725485450967279,"score_spread":0.2572584387856577,"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."}}