{"id":"W2150052759","doi":"10.1175/2007mwr2323.1","title":"Sampling Errors in Ensemble Kalman Filtering. Part I: Theory","year":2008,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Ensemble Kalman filter; Kalman filter; Covariance; Covariance matrix; Fast Kalman filter; Covariance intersection; Computer science; Context (archaeology); Mathematics; Invariant extended Kalman filter; Extended Kalman filter; Statistics; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005555052,0.0001291188,0.0002962717,0.00003963591,0.0001195956,0.000008690222,0.0001707682,0.00004067069,0.007477958],"category_scores_gemma":[0.0001025447,0.0000909142,0.0000844177,0.0001876333,0.00005011036,0.0000976044,0.00001030896,0.0001219472,0.0005011671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003994553,"about_ca_system_score_gemma":0.00001427656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001433068,"about_ca_topic_score_gemma":0.0002997726,"domain_scores_codex":[0.9988657,0.0001938712,0.000309563,0.0002300057,0.0001396259,0.0002612814],"domain_scores_gemma":[0.9994137,0.0001879156,0.00005887248,0.0002354899,0.00001225318,0.00009173799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008141419,0.0001658177,0.6285493,0.0009106903,0.00006068168,0.0002145658,0.001209234,0.03373687,0.00004923361,0.00165124,0.003565734,0.3298053],"study_design_scores_gemma":[0.0003115369,0.0001453837,0.4241462,0.0008689371,0.00003194539,0.00001391508,0.00003120953,0.001788837,0.000006127512,0.00884439,0.5633915,0.0004200453],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5507081,0.3179435,0.0002656991,0.0005774884,0.000251195,0.0007188465,0.00004567169,0.000101997,0.1293875],"genre_scores_gemma":[0.9732355,0.02139496,0.00119984,0.002656993,0.00008325183,0.000009924977,0.00007399437,0.000006740395,0.001338745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5598258,"threshold_uncertainty_score":0.9934294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07862381201085045,"score_gpt":0.2688848527945632,"score_spread":0.1902610407837128,"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."}}