{"id":"W2461495608","doi":"10.1007/s13131-015-0757-x","title":"Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework","year":2016,"lang":"en","type":"article","venue":"Acta Oceanologica Sinica","topic":"Climate variability and models","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Ensemble Kalman filter; Particle filter; Kalman filter; Gaussian; Extended Kalman filter; Mathematics; Bayesian probability; Data assimilation; Algorithm; Filter (signal processing); Computer science; Applied mathematics; Statistics; Physics","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":[0.003302323,0.0007935759,0.001135736,0.0009513583,0.0004536884,0.001066665,0.0008612575,0.001204315,0.002199754],"category_scores_gemma":[0.007761407,0.0003159377,0.0009173869,0.0009191161,0.0002363232,0.002558806,0.0007641722,0.0009119244,0.0007355392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002998315,"about_ca_system_score_gemma":0.001989858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01112605,"about_ca_topic_score_gemma":0.01425338,"domain_scores_codex":[0.9987656,0.0004155463,0.00008311908,0.0001973787,0.0004110024,0.0001273468],"domain_scores_gemma":[0.9976823,0.0009210852,0.00008053766,0.0002544024,0.0009734028,0.00008826861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000901597,0.000286121,0.009770516,0.0003455624,0.0005663004,0.00008100935,0.0001036635,0.3837407,0.006301928,0.01122955,0.0037151,0.5829579],"study_design_scores_gemma":[0.00004177321,0.00008881337,0.003283938,0.00002301962,0.000123998,0.00005125546,0.00002649197,0.9907982,0.001809079,0.001907096,0.001807965,0.00003845764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09285817,0.001910203,0.896893,0.0004657296,0.000306052,0.00004951975,0.0003638128,0.001319692,0.005833845],"genre_scores_gemma":[0.6239162,0.001229421,0.3697114,0.0001516639,0.0001755688,0.00007363253,0.0009752729,0.0002690196,0.00349775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01112605,"threshold_uncertainty_score":0.02212256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03429377887927743,"score_gpt":0.2848759833798894,"score_spread":0.250582204500612,"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."}}