{"id":"W2006605572","doi":"10.1175/1520-0493(2002)130<2791:esbame>2.0.co;2","title":"Ensemble Size, Balance, and Model-Error Representation in an Ensemble Kalman Filter*","year":2002,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":296,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage","funders":"","keywords":"Ensemble Kalman filter; Data assimilation; Kalman filter; Forcing (mathematics); Ensemble forecasting; Context (archaeology); Meteorology; Ensemble average; Radiosonde; Environmental science; Computer science; Mathematics; Statistics; Climatology; Geology; Extended Kalman filter; Geography","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":[0.002412335,0.0006078268,0.0008920922,0.0006024678,0.0004500498,0.001329214,0.0008458497,0.0009812808,0.001202421],"category_scores_gemma":[0.008528172,0.0003935255,0.0004302783,0.001015091,0.0007565543,0.005662558,0.001689956,0.001207388,0.0002848493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005352772,"about_ca_system_score_gemma":0.0006258744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004203703,"about_ca_topic_score_gemma":0.003521069,"domain_scores_codex":[0.9992194,0.0002485905,0.00006517266,0.0001515204,0.0002676013,0.00004764725],"domain_scores_gemma":[0.9977406,0.001288609,0.0002823115,0.0002324976,0.0004116663,0.00004428983],"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":[0.00008780092,0.00003248591,0.003275985,0.00009869002,0.00009110212,0.00006268227,0.0001416328,0.652976,0.0028809,0.2367044,0.002079685,0.1015686],"study_design_scores_gemma":[0.00001051058,0.00004211267,0.001297616,0.00002681292,0.00003159662,0.00003250666,0.00001968289,0.9045636,0.0008947748,0.09034793,0.00270739,0.00002552743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01758604,0.0009244569,0.9768837,0.0004280901,0.000126979,0.00001283994,0.0001322064,0.0001368908,0.003768789],"genre_scores_gemma":[0.7754238,0.002771368,0.2154404,0.0001996409,0.0005290601,0.000182985,0.0005063312,0.0002125943,0.004733867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004203703,"threshold_uncertainty_score":0.01275778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06838461974096545,"score_gpt":0.2799004922183927,"score_spread":0.2115158724774273,"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."}}