{"id":"W2143576520","doi":"10.1002/2014ms000373","title":"A modified ensemble Kalman particle filter for non‐Gaussian systems with nonlinear measurement functions","year":2014,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"National Natural Science Foundation of China","keywords":"Ensemble Kalman filter; Particle filter; Data assimilation; Kalman filter; Nonlinear system; Algorithm; Resampling; Extended Kalman filter; Computer science; Gaussian; Covariance; Invariant extended Kalman filter; Computation; Applied mathematics; Mathematics; Artificial intelligence; Physics; Statistics; Meteorology","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":[0.001688075,0.0001511273,0.0004078282,0.000118662,0.0001601708,0.00009558808,0.0001659067,0.00005950888,0.0000145197],"category_scores_gemma":[0.0001120728,0.00009653089,0.00007961002,0.0001768678,0.00002898759,0.0004783829,0.0000042617,0.0001745913,0.00001042581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001258978,"about_ca_system_score_gemma":0.00005370313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000121691,"about_ca_topic_score_gemma":0.0005083621,"domain_scores_codex":[0.9980444,0.0001513074,0.0007535586,0.0001952125,0.0005325484,0.0003229564],"domain_scores_gemma":[0.9988184,0.0001914476,0.000329538,0.0001757651,0.000320144,0.0001646993],"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":[0.0001969035,0.00003015979,0.008732065,0.00006983564,0.00002008458,0.000002615913,0.00008901324,0.9889786,0.00007057521,0.00009927323,0.000009865253,0.001701026],"study_design_scores_gemma":[0.001012476,0.0007810075,0.0008262984,0.000197355,0.00002626215,0.00002267437,0.0003020505,0.9936206,0.000006824328,0.0002638847,0.002803045,0.000137543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.384016,0.002253944,0.6109699,0.00006467972,0.000866409,0.0004016852,0.00001602866,0.00001417707,0.0013972],"genre_scores_gemma":[0.9950926,0.00004554483,0.004274559,0.00003282052,0.0004463101,0.00000721668,0.000006673305,0.000006518076,0.00008773439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6110767,"threshold_uncertainty_score":0.3936415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0503414054190761,"score_gpt":0.2494931592616126,"score_spread":0.1991517538425365,"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."}}