{"id":"W3007213720","doi":"10.1007/s00332-020-09616-x","title":"Particle Filters with Nudging in Multiscale Chaotic Systems: With Application to the Lorenz ’96 Atmospheric Model","year":2020,"lang":"en","type":"article","venue":"Journal of Nonlinear Science","topic":"Climate variability and models","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research","keywords":"Particle filter; Ensemble Kalman filter; Chaotic; Nonlinear system; Dimensionality reduction; Curse of dimensionality; Kalman filter; Auxiliary particle filter; Nonlinear filter; Filter (signal processing); Mathematics; Statistical physics; Dimension (graph theory); Computer science; Control theory (sociology); Extended Kalman filter; Filter design; Physics; Artificial intelligence; 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":[0.0007191765,0.00007858359,0.0001232059,0.000008317042,0.0001132034,0.00006604038,0.0004619921,0.00001446836,0.0000117202],"category_scores_gemma":[0.00005586356,0.0000431818,0.00001777618,0.0009133983,0.0002537033,0.0004278223,0.00009695726,0.0001331943,0.00002725152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001306724,"about_ca_system_score_gemma":0.00006438999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008711081,"about_ca_topic_score_gemma":0.00008449121,"domain_scores_codex":[0.9987681,0.0000248326,0.0002388171,0.0002100263,0.0005328676,0.0002253757],"domain_scores_gemma":[0.9994295,0.00003479489,0.000137216,0.0001756587,0.00003132961,0.0001915627],"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":[0.00005516816,0.00004686137,0.01793246,0.000005445062,0.000001294133,0.000003934789,0.001721311,0.9729444,0.006516102,0.00001552357,0.00001390217,0.0007435501],"study_design_scores_gemma":[0.0002492772,0.0002039166,0.004220508,0.00002852108,0.000006603158,0.00002316759,0.0004543276,0.9941267,0.0004427129,0.000008653516,0.0001648905,0.00007074539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.875555,0.00001180006,0.1202865,0.003822582,0.0000202192,0.0002080958,0.000001659813,0.000006366036,0.00008784624],"genre_scores_gemma":[0.9709583,0.00000412394,0.02842146,0.0005614778,0.00003111535,0.000006569197,1.181777e-7,0.000005725902,0.00001112626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09540333,"threshold_uncertainty_score":0.1760903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751083055590659,"score_gpt":0.2356564633783732,"score_spread":0.2181456328224666,"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."}}