{"id":"W4413468435","doi":"10.1109/icjece.2025.3592219","title":"HEES-Based IFVR for Energy-Saving Application Using DC–DC Converter","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001388065,0.0003267559,0.0002795999,0.0004252572,0.0003046066,0.0007081925,0.0007806125,0.0002656818,0.009028385],"category_scores_gemma":[0.0001801745,0.0001052033,0.0001773691,0.0004372235,0.0002145054,0.0006218988,0.0002868727,0.0004359637,0.001894119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002907828,"about_ca_system_score_gemma":0.0001911674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006758693,"about_ca_topic_score_gemma":0.001622608,"domain_scores_codex":[0.9998341,0.00001792121,0.00001097773,0.00004004046,0.00007087321,0.00002605915],"domain_scores_gemma":[0.9998913,0.00001497411,0.00001399929,0.00003037989,0.00004232021,0.000007076721],"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":[0.0009481368,0.0003271223,0.001720905,0.000917435,0.00008512281,0.0008359091,0.0003049112,0.02249037,0.42545,0.01476577,0.02796118,0.5041932],"study_design_scores_gemma":[0.0001883037,0.0009172421,0.005127429,0.0002073194,0.0001212228,0.001931446,0.0002916933,0.267113,0.5297695,0.007714471,0.1865209,0.00009752986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1855746,0.002768522,0.6668911,0.0008972869,0.000797123,0.0005011824,0.001066758,0.0118442,0.1296593],"genre_scores_gemma":[0.9598029,0.0004854842,0.02271389,0.0002090937,0.0000709421,0.00006688813,0.0002749205,0.00009261935,0.01628316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009028385,"threshold_uncertainty_score":0.03020293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00453114873710223,"score_gpt":0.1703015567416404,"score_spread":0.1657704080045382,"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."}}