{"id":"W3107287639","doi":"10.1002/047134608x.w8395","title":"Soft‐Switching in Power Electronic Converters–An Introduction","year":2019,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Electrical and Electronics Engineering","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Converters; Voltage; Power (physics); Electrical engineering; Flyback transformer; Switching time; Commutation cell; Electronic engineering; Computer science; Engineering; Switched-mode power supply; Physics; Transformer; Constant power circuit","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":[0.0001376578,0.0004746322,0.0005916891,0.0006718633,0.00001565053,0.00001831654,0.0002341307,0.0003194086,0.00006434418],"category_scores_gemma":[0.00004151669,0.0005334268,0.00007520582,0.0005710463,0.00001933934,0.0002172047,0.00003071113,0.0009405554,0.00001413043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003413774,"about_ca_system_score_gemma":0.00008828912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000231727,"about_ca_topic_score_gemma":0.00004090063,"domain_scores_codex":[0.9978213,0.00002093594,0.0004142542,0.0004911092,0.0002174346,0.001034908],"domain_scores_gemma":[0.9993507,0.00005576031,0.00007610712,0.0003635011,0.00001759276,0.000136382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001524912,0.0003068317,0.0010848,0.001202632,0.0007331447,0.00003484521,0.0008384177,0.02305106,0.03377017,0.06209124,0.01773123,0.8590031],"study_design_scores_gemma":[0.001007637,0.000481498,0.0001700925,0.0001394972,0.00005779398,0.00004796743,0.00001614137,0.5925202,0.0002252465,0.0004113883,0.4037853,0.001137277],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02333593,0.09516824,0.8262537,0.0002520854,0.002795361,0.002119445,0.00001721302,0.003554259,0.04650381],"genre_scores_gemma":[0.9626316,0.02576296,0.001288055,0.0000458486,0.0007228919,0.00007149082,0.00003649602,0.001227513,0.008213146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9392956,"threshold_uncertainty_score":0.9997118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001807286815129952,"score_gpt":0.1744035355586608,"score_spread":0.1725962487435308,"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."}}