{"id":"W3173624615","doi":"10.20944/preprints202106.0474.v1","title":"GMDH Neural Networks - Based Modeling of Variable Power Inductor","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Inductor; Inductance; Artificial neural network; Control theory (sociology); Power (physics); Nonlinear system; Voltage; Computer science; Variable (mathematics); Electronic engineering; Topology (electrical circuits); Engineering; Mathematics; Artificial intelligence; Electrical engineering; Physics","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":[0.0004573902,0.0004257565,0.0004431012,0.0003894794,0.0001834617,0.0005629712,0.001113932,0.0006076426,0.001719003],"category_scores_gemma":[0.001094423,0.0002833626,0.0004853531,0.0004887203,0.0003947495,0.0007790008,0.0003571972,0.0006204864,0.0003983097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007486042,"about_ca_system_score_gemma":0.0003919887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003713496,"about_ca_topic_score_gemma":0.003537812,"domain_scores_codex":[0.9997968,0.00007088849,0.000008957627,0.00003903396,0.00006841918,0.00001598048],"domain_scores_gemma":[0.999729,0.0001316212,0.00002761012,0.00002892093,0.00007470191,0.000008057518],"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":[0.00002615837,0.00001044483,0.0002762697,0.00007030745,0.00001744798,0.00004203345,0.00003648156,0.9739391,0.002912253,0.004960454,0.0003173216,0.01739169],"study_design_scores_gemma":[8.230301e-7,0.000002691872,0.00003531207,0.00000206858,0.000001406478,0.000005010619,0.000001309932,0.9987229,0.0003312288,0.0006395499,0.0002565415,0.000001257496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01961684,0.0005539988,0.9733435,0.0001002947,0.00005763712,0.00003986137,0.0001152676,0.0003879515,0.005784737],"genre_scores_gemma":[0.8158848,0.0007082545,0.1720811,0.00008057643,0.00004565194,0.0002010437,0.0003031263,0.0001387282,0.01055684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003713496,"threshold_uncertainty_score":0.007383764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1270995749367345,"score_gpt":0.3223365937671903,"score_spread":0.1952370188304558,"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."}}