{"id":"W2085917899","doi":"10.1109/sam.2014.6882379","title":"Distributed ensemble Kalman filtering","year":2014,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Kalman filter; Gossip; Computer science; Wireless sensor network; Nonlinear system; Extended Kalman filter; Overhead (engineering); Distributed algorithm; State (computer science); Ensemble Kalman filter; Filter (signal processing); Fast Kalman filter; Invariant extended Kalman filter; Distributed computing; Algorithm; Artificial intelligence; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001149264,0.000627636,0.001140448,0.0005538741,0.0004294632,0.0009220673,0.001439901,0.001263963,0.001455906],"category_scores_gemma":[0.004120419,0.0003178116,0.0007785633,0.000926338,0.0005400137,0.002356346,0.001211062,0.001416823,0.0005309155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005263702,"about_ca_system_score_gemma":0.0007447837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00269332,"about_ca_topic_score_gemma":0.002762916,"domain_scores_codex":[0.9990261,0.0002071303,0.00004073752,0.000230197,0.0004081092,0.00008772424],"domain_scores_gemma":[0.9981366,0.000787102,0.0001955778,0.0004002777,0.0004314471,0.00004892159],"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":[0.00006881596,0.00004977119,0.001403466,0.00008873401,0.0001117507,0.00008678826,0.00008658547,0.7679378,0.004892852,0.06949964,0.002283917,0.1534898],"study_design_scores_gemma":[0.000005730681,0.00002399904,0.00014509,0.000005278645,0.00001114754,0.00002853269,0.000006446816,0.9859634,0.0008399559,0.011155,0.001808978,0.00000653343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002191215,0.0001522577,0.9966252,0.00005458123,0.00004103554,0.00000714844,0.00001980379,0.0001188806,0.0007898617],"genre_scores_gemma":[0.5299467,0.00148023,0.461694,0.0002176256,0.0003561427,0.0001769421,0.0003963337,0.00008640344,0.005645505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00269332,"threshold_uncertainty_score":0.006078005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271321522531382,"score_gpt":0.221775266204011,"score_spread":0.2090620509786972,"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."}}