{"id":"W2007052123","doi":"10.1021/ie900197s","title":"Constrained Nonlinear State Estimation Using Ensemble Kalman Filters","year":2010,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Ensemble Kalman filter; Benchmark (surveying); Kalman filter; Nonlinear system; Computer science; Particle filter; Mathematical optimization; State (computer science); Extended Kalman filter; Process (computing); State variable; Moving horizon estimation; Algorithm; Control theory (sociology); Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008117193,0.0006255184,0.0009661668,0.0004953093,0.0003096909,0.0007665693,0.0007910678,0.0007196029,0.0008668561],"category_scores_gemma":[0.003454131,0.0004333197,0.0008045837,0.000607215,0.0004470028,0.001469751,0.001028916,0.000915975,0.0002279227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004991702,"about_ca_system_score_gemma":0.001024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009981685,"about_ca_topic_score_gemma":0.009304895,"domain_scores_codex":[0.9993449,0.000205132,0.00003934562,0.0001546555,0.0002088412,0.00004712743],"domain_scores_gemma":[0.9988655,0.0005916357,0.0001701607,0.0001491445,0.000200348,0.00002323112],"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":[0.00003944621,0.00002101879,0.0006120504,0.00004095449,0.0000685765,0.00004260214,0.00004674674,0.9161914,0.003154918,0.01125161,0.0004256072,0.06810507],"study_design_scores_gemma":[0.000002676333,0.000006645514,0.0001648821,0.000003248143,0.00000493879,0.000007455737,0.000002550372,0.9963365,0.0005457416,0.002609308,0.0003097778,0.000006254121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00505843,0.00008708986,0.99435,0.00002390366,0.000007919445,0.000005978737,0.00001814483,0.0001035166,0.0003449941],"genre_scores_gemma":[0.5268303,0.0007270474,0.4692709,0.00007932307,0.00007533816,0.000160379,0.0003862299,0.00009084666,0.002379633],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009981685,"threshold_uncertainty_score":0.01984715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07902707386503359,"score_gpt":0.331128836149622,"score_spread":0.2521017622845884,"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."}}