{"id":"W1998538247","doi":"10.1109/tevc.2012.2197400","title":"A Novel Genetic Programming Approach for Frequency-Dependent Modeling","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Equivalent circuit; Genetic programming; Computer science; Key (lock); Electronic circuit; Passivity; Process (computing); Frequency response; Genetic algorithm; Electrical element; Power electronics; Topology (electrical circuits); Electronic engineering; Engineering; Voltage; Machine learning; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00004987529,0.0002066546,0.0001539923,0.0002716102,0.0001537933,0.00004687135,0.0001482757,0.0001573542,0.0000161376],"category_scores_gemma":[0.000005043675,0.0002335378,0.0001039443,0.000239761,0.00004094065,0.000314497,0.000001049427,0.0002171656,0.00002838493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002887785,"about_ca_system_score_gemma":0.00002921079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009384428,"about_ca_topic_score_gemma":0.000005169158,"domain_scores_codex":[0.9988968,0.00001175331,0.0003079764,0.0002968345,0.0001941095,0.000292529],"domain_scores_gemma":[0.9995274,0.00006069732,0.000032327,0.0001843869,0.0001407606,0.00005440855],"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":[0.000002968681,0.00007860282,0.000004648485,0.00004554841,0.00004184802,1.803572e-7,0.00006254772,0.9039504,0.05667127,0.00002974765,0.00004882391,0.03906344],"study_design_scores_gemma":[0.0004047457,0.00006364031,0.0001042367,0.00001515083,0.00002679952,0.0000296825,0.0002156863,0.9942937,0.0033142,0.001285673,0.000003883702,0.0002425296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1529406,0.0001715418,0.844101,0.00003077034,0.0004130222,0.0009348397,0.00002532899,0.00130185,0.00008114321],"genre_scores_gemma":[0.7513585,0.00001015026,0.2477074,0.00001337376,0.00003788818,0.0007973233,0.00001947331,0.00004171809,0.00001415844],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5984179,"threshold_uncertainty_score":0.9523396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02612461641044826,"score_gpt":0.2269474220565917,"score_spread":0.2008228056461434,"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."}}