{"id":"W4385832172","doi":"10.1109/tac.2023.3305191","title":"On Approximation of System Behavior From Large Noisy Data Using Statistical Properties of Measurement Noise","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Australian Research Council","keywords":"Noise (video); Kalman filter; Covariance; Noise measurement; White noise; Algorithm; Mathematics; Representation (politics); Linear system; Control theory (sociology); Computer science; Artificial intelligence; Noise reduction; Statistics","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":[0.000537109,0.0001558073,0.0004009456,0.0002096355,0.00006783263,0.00002801238,0.0001908386,0.00007043126,0.00002965415],"category_scores_gemma":[0.00002264253,0.0001423282,0.00006507681,0.0002040913,0.00002187926,0.0001404683,0.000001457251,0.00008009187,0.00004638437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628417,"about_ca_system_score_gemma":0.00003941782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002201692,"about_ca_topic_score_gemma":0.0000798356,"domain_scores_codex":[0.9982716,0.00009255343,0.000690026,0.0002097873,0.0005569899,0.0001789854],"domain_scores_gemma":[0.9989565,0.0001007537,0.0001315479,0.0006409818,0.0001217934,0.0000484448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009880698,0.0004991716,0.0000299532,0.002278509,0.0005006749,0.000004221021,0.0006694536,0.1376063,0.8351703,0.0002673846,0.0001280414,0.02274716],"study_design_scores_gemma":[0.001364287,0.00003710878,0.001277213,0.0006648597,0.0003237161,0.000001070294,0.0002092075,0.9823308,0.01366005,0.000006940005,0.000006307452,0.0001184311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3481094,0.00004230642,0.6493074,0.000008412253,0.0004600243,0.0007272941,0.001048303,0.0002825723,0.00001424873],"genre_scores_gemma":[0.9994307,0.000002488811,0.0003025171,0.00000247172,0.00002310994,0.0001649628,0.00003451167,0.00003134322,0.000007937121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8447245,"threshold_uncertainty_score":0.5803974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05466577158805903,"score_gpt":0.2471067423583798,"score_spread":0.1924409707703207,"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."}}