{"id":"W4385723450","doi":"10.21203/rs.3.rs-3189614/v1","title":"Filtered Low-power Multi-high-gain Observer Design for a Class of Nonlinear Systems","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nonlinear system; Class (philosophy); High-gain antenna; Observer (physics); Control theory (sociology); Power (physics); Computer science; Engineering; Physics; Electrical engineering; Artificial intelligence; Control (management)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003943647,0.0006011365,0.001323275,0.0007885974,0.0001160807,0.0001897292,0.001187009,0.0008909036,0.00002766174],"category_scores_gemma":[0.00151337,0.0005937248,0.0004705828,0.0005139646,0.0001567471,0.0001143083,0.0007366411,0.001607648,0.0002520494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006141461,"about_ca_system_score_gemma":0.0004228822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004490391,"about_ca_topic_score_gemma":0.00009543754,"domain_scores_codex":[0.9944879,0.000846912,0.001153296,0.0008732309,0.001401246,0.001237457],"domain_scores_gemma":[0.993757,0.00239633,0.0002071089,0.001522325,0.001809577,0.000307627],"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":[0.000281188,0.0002287819,0.0002189306,0.01693995,0.001001984,0.0001144872,0.0005594294,0.9549984,0.006494213,0.0003175816,0.01858157,0.0002634609],"study_design_scores_gemma":[0.001842538,0.0002633174,0.001065594,0.003960011,0.00003406129,0.00000224722,0.000504867,0.9865418,0.001209636,0.00008493011,0.00390428,0.0005867298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05313069,0.006598115,0.9027378,0.0002649409,0.006451427,0.01972597,0.008981421,0.001897609,0.0002120384],"genre_scores_gemma":[0.9650433,0.0002095168,0.02538725,0.000005319828,0.001306861,0.003137837,0.0006441774,0.0006408477,0.003624893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9119126,"threshold_uncertainty_score":0.9996514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1946111038528215,"score_gpt":0.3766051451663125,"score_spread":0.181994041313491,"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."}}