{"id":"W4386113334","doi":"10.1109/tpel.2023.3306979","title":"Energy Conservation Versus Charge Conservation Law for Modeling and Analyzing Cell Equalizers","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Battery (electricity); Equalization (audio); Energy conservation; Capacitance; Reliability (semiconductor); Conservation law; Conservation of energy; Charge conservation; Engineering; Renewable energy; Power (physics); Electrical engineering; Process (computing); Computer science; Charge (physics); Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001403155,0.0001685193,0.0001518734,0.0002614238,0.0002049995,0.00003882808,0.0001155076,0.000145282,0.00001379129],"category_scores_gemma":[0.000006777705,0.0001988623,0.00005800245,0.0005166887,0.00004336084,0.0002261722,0.000001739444,0.0002691878,0.00001165843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002247391,"about_ca_system_score_gemma":0.00003490807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003377539,"about_ca_topic_score_gemma":0.0002331841,"domain_scores_codex":[0.9988778,0.00001678653,0.0002113354,0.0002427053,0.0001515751,0.0004998063],"domain_scores_gemma":[0.9994037,0.0002360375,0.00002398646,0.0002174478,0.00007167816,0.00004711117],"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":[0.0003055599,0.00004756896,0.000002691984,0.0001139587,0.000155021,0.000002724593,0.0001708814,0.8867063,0.09248528,0.00939503,0.001053522,0.009561449],"study_design_scores_gemma":[0.0008215262,0.0001818958,3.008022e-7,0.00001084134,0.00001884288,8.60198e-7,0.00007299167,0.8502575,0.1402654,0.001045925,0.007132789,0.0001911493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07526093,0.0002402026,0.9222556,0.0004985112,0.0002763273,0.0001972151,0.00003416082,0.0009925391,0.0002444562],"genre_scores_gemma":[0.9976145,0.001025125,0.0007747533,0.0001516383,0.000008414196,0.0001609821,0.00002779164,0.00006055018,0.0001762259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9223536,"threshold_uncertainty_score":0.8109369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0299333481254814,"score_gpt":0.2779029017258036,"score_spread":0.2479695536003222,"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."}}