{"id":"W2012824567","doi":"10.1016/j.eswa.2011.07.103","title":"Modeling of the charging characteristic of linear-type superconducting power supply using granular-based radial basis function neural networks","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Frequency Control in Power Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Cluster analysis; Magnet; Fuzzy logic; Power (physics); Basis (linear algebra); Voltage; Superconductivity; Function (biology); Superconducting magnet; Topology (electrical circuits); Artificial intelligence; Physics; Mathematics; Mechanical engineering; Electrical engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002946241,0.0003403458,0.000504335,0.0002356406,0.000282557,0.000646458,0.0007009606,0.0007226425,0.0009341345],"category_scores_gemma":[0.0008426773,0.0002681515,0.0003925779,0.000327993,0.0005107759,0.0008013857,0.000293965,0.000488334,0.0001254073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006843478,"about_ca_system_score_gemma":0.0004451009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009017957,"about_ca_topic_score_gemma":0.006225787,"domain_scores_codex":[0.9998797,0.00003362519,0.000006331739,0.0000210179,0.00004068105,0.00001860528],"domain_scores_gemma":[0.9997496,0.0001028145,0.0000440966,0.00001645185,0.0000715487,0.00001555454],"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":[0.00003985429,0.00001004262,0.0003475039,0.00001843103,0.00001087861,0.00006890716,0.00001832024,0.9934611,0.001536636,0.001856752,0.0001297716,0.002501711],"study_design_scores_gemma":[7.556962e-7,0.000001791206,0.00005038312,4.567189e-7,7.618463e-7,0.000002526549,0.000001059648,0.9997069,0.00006085298,0.0001586836,0.00001483861,8.476507e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3459004,0.0005337751,0.6370404,0.0004250109,0.00009905413,0.00006426725,0.0001164444,0.0005725942,0.01524807],"genre_scores_gemma":[0.9960101,0.0000757905,0.002571666,0.00001417352,0.000007030413,0.00001045122,0.0000201257,0.00001269493,0.001277966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009017957,"threshold_uncertainty_score":0.01793092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784740974738042,"score_gpt":0.2178714034581652,"score_spread":0.1900239937107848,"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."}}