{"id":"W2027530021","doi":"10.1109/tpel.2011.2173353","title":"A General Approach for Quantifying the Benefit of Distributed Power Electronics for Fine Grained MPPT in Photovoltaic Applications Using 3-D Modeling","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Photovoltaic system; Maximum power point tracking; Power electronics; Irradiance; Electronics; Maximum power principle; Solar micro-inverter; Solar irradiance; Energy harvesting; Engineering; Computer science; Automotive engineering; Electrical engineering; Electronic engineering; Energy (signal processing); Meteorology; 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":[0.0005542235,0.0003924949,0.0005038274,0.0003568537,0.000356631,0.00003465594,0.0004911946,0.0002828034,0.0000292076],"category_scores_gemma":[0.00001832881,0.0003603007,0.0003649556,0.000918047,0.00006764805,0.0001833434,0.000004013579,0.0003927547,8.239199e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000579795,"about_ca_system_score_gemma":0.0003017931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005573833,"about_ca_topic_score_gemma":0.000930201,"domain_scores_codex":[0.9974098,0.00006904081,0.000826491,0.0005796608,0.000268886,0.0008461486],"domain_scores_gemma":[0.9983446,0.0001564856,0.0002993137,0.0007231596,0.0003994153,0.00007702791],"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":[0.0005628253,0.0008784147,0.000008507507,0.00008296789,0.0002204096,2.287448e-7,0.0006895572,0.9051272,0.0741882,0.01745953,0.0000313278,0.0007508194],"study_design_scores_gemma":[0.001003183,0.0003208201,0.0000017258,0.00002640123,0.0001068181,0.000009799027,0.0001238251,0.766355,0.2280957,0.002723119,0.000909025,0.0003245396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03397896,0.0004228872,0.9618606,0.00001874866,0.00008610865,0.00297414,0.0002733964,0.0002171744,0.0001679715],"genre_scores_gemma":[0.9333585,0.00009728551,0.06298668,0.00005209431,0.00001684401,0.00316612,0.0001237332,0.0001220114,0.00007671218],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8993796,"threshold_uncertainty_score":0.9998849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05068198350341947,"score_gpt":0.280663802419318,"score_spread":0.2299818189158986,"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."}}