{"id":"W2148887226","doi":"10.1109/cdc.1997.657078","title":"Gain/phase margin improvement using static generalized sampled-data hold functions","year":2002,"lang":"en","type":"article","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Phase margin; Control theory (sociology); Margin (machine learning); Controller (irrigation); Computer science; Function (biology); Mathematics; Control (management); Bandwidth (computing); Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006862569,0.0001426072,0.0001409741,0.00004458038,0.0001706133,0.0000711128,0.0001609015,0.0000212865,0.0197238],"category_scores_gemma":[0.000001594189,0.0001230901,0.00006085787,0.0001338681,0.00002477843,0.0002044518,0.00009755028,0.0001115732,0.00009906623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001946663,"about_ca_system_score_gemma":0.00001245095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003611889,"about_ca_topic_score_gemma":0.000004551719,"domain_scores_codex":[0.9990374,0.00002938215,0.0002289394,0.0003105618,0.0001283505,0.0002653865],"domain_scores_gemma":[0.9992482,0.00001976301,0.00006740133,0.0005168508,0.00002822026,0.0001195499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007226459,0.002204312,0.0008447907,0.00003094924,0.0004218968,0.000005179439,0.0002245835,0.02987278,0.03996896,0.01783129,0.4751673,0.4333557],"study_design_scores_gemma":[0.001439996,0.00004869287,0.000004879406,0.000007547746,0.00004121373,0.00000108485,0.000146514,0.970807,0.0006642438,0.0004547136,0.02619511,0.0001890736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4168748,0.00005648288,0.57269,0.0007329068,0.0005051362,0.0004018068,0.0002513007,0.00009238206,0.00839515],"genre_scores_gemma":[0.9814765,0.000009560586,0.004240872,0.0003834735,0.0005874296,0.00001863932,0.0003128811,0.00002192348,0.01294876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9409342,"threshold_uncertainty_score":0.9811723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471204211156934,"score_gpt":0.329116714325536,"score_spread":0.1819962932098425,"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."}}