{"id":"W2238676483","doi":"10.1109/ias.1992.244326","title":"Source reactance lossless switch (SRLS) for soft-switching converters with constant switching frequency","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Reactance; Lossless compression; Converters; Rectifier (neural networks); Zero (linguistics); Network topology; Topology (electrical circuits); Computer science; Voltage; Control theory (sociology); Electronic engineering; Electrical engineering; Engineering; Control (management); Algorithm; Computer network","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":[0.0001948438,0.0004197327,0.0002283181,0.0003151183,0.0001981041,0.0008220111,0.0005170807,0.0002836799,0.004458193],"category_scores_gemma":[0.0003124987,0.0001493865,0.0002891055,0.0002906504,0.0004218581,0.0009547239,0.0001995934,0.0004607728,0.0008742198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000307424,"about_ca_system_score_gemma":0.0002179998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001096619,"about_ca_topic_score_gemma":0.000208109,"domain_scores_codex":[0.9998881,0.00002171647,0.000006224544,0.00001732577,0.00005989554,0.0000067901],"domain_scores_gemma":[0.999887,0.00004386755,0.00002508283,0.00002228731,0.00001811171,0.000003705289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001830453,0.0000742407,0.0005534639,0.0009638839,0.00005207491,0.000528363,0.0002715788,0.02668763,0.2782752,0.3313025,0.006256728,0.3548512],"study_design_scores_gemma":[0.000169213,0.0008772851,0.001116864,0.0001825622,0.0001616452,0.003344179,0.0001259727,0.4102692,0.2726662,0.145363,0.1656405,0.00008343763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0268504,0.001971424,0.9482161,0.0002925405,0.0001678109,0.00007201522,0.00005111983,0.001785787,0.0205928],"genre_scores_gemma":[0.7403463,0.002787454,0.2332751,0.0002568662,0.0002070276,0.0001431053,0.0002228427,0.0002410789,0.02252019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004458193,"threshold_uncertainty_score":0.01491416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006913855222901765,"score_gpt":0.1977063847276677,"score_spread":0.1907925295047659,"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."}}