{"id":"W4206646745","doi":"10.1109/tpel.2021.3134597","title":"A Generalized Method for Comprehension of Switched-Capacitor High Step-Up Converters Including Coupled Inductors and Voltage Multiplier Cells","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voltage multiplier; Inductor; Converters; Switched capacitor; Capacitor; Multiplier (economics); Electronic engineering; Voltage; Control theory (sociology); Lagrange multiplier; Computer science; Topology (electrical circuits); Electrical engineering; Engineering; Mathematics; Voltage source; Dropout voltage; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001305707,0.0003252446,0.0004905093,0.0001683419,0.0001272047,0.00002350155,0.0001169371,0.0001948304,0.00006146738],"category_scores_gemma":[0.000007055065,0.0003617612,0.0001619197,0.000277825,0.00005060914,0.0001778796,0.000003063708,0.0004090814,0.000003563032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002613538,"about_ca_system_score_gemma":0.00008795036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000230518,"about_ca_topic_score_gemma":0.00005508738,"domain_scores_codex":[0.9984462,0.00004693454,0.0004125205,0.0004005094,0.0002111814,0.0004826392],"domain_scores_gemma":[0.9989475,0.0003424421,0.00007972141,0.0003570641,0.0001495949,0.0001236944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000137887,0.00006598004,0.000001999911,0.0001069664,0.0002687567,0.000002897576,0.0005540588,0.01070545,0.9583721,0.0001206281,0.00006872905,0.02959452],"study_design_scores_gemma":[0.001984342,0.000112161,0.000007315194,0.00002956807,0.00009802832,0.000008634484,0.0001387741,0.4839599,0.5119624,0.00007405898,0.001356669,0.0002682153],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1730179,0.0003247769,0.8245915,0.00003190708,0.001450505,0.0003672124,0.0000591271,0.0001534122,0.000003615096],"genre_scores_gemma":[0.9627501,0.0003404668,0.03647299,0.00009569897,0.00001962223,0.00006449215,0.0000120292,0.00009858772,0.0001459637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7897322,"threshold_uncertainty_score":0.9998834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474828647317777,"score_gpt":0.2532985475773035,"score_spread":0.2385502611041257,"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."}}