{"id":"W2038010438","doi":"10.1175/mwr-d-12-00353.1","title":"Examination of Situation-Dependent Background Error Covariances at the Convective Scale in the Context of the Ensemble Kalman Filter","year":2013,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ensemble Kalman filter; Context (archaeology); Kalman filter; Data assimilation; Convection; Computer science; Scale (ratio); Meteorology; Forcing (mathematics); Radar; Precipitation; Filter (signal processing); Environmental science; Statistical physics; Climatology; Extended Kalman filter; Geology; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001722015,0.0002318897,0.0003521478,0.0004868813,0.0002544813,0.000751787,0.0002686413,0.0002744458,0.0004669945],"category_scores_gemma":[0.006785239,0.0002404099,0.0003324125,0.0004730688,0.0002840965,0.001012852,0.000388036,0.000467386,0.00006689407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000453704,"about_ca_system_score_gemma":0.0009513408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01854719,"about_ca_topic_score_gemma":0.01490134,"domain_scores_codex":[0.9996845,0.00008184022,0.00001601471,0.0000769549,0.0001001321,0.00004067206],"domain_scores_gemma":[0.9976427,0.00126762,0.000324427,0.0001738508,0.0005395591,0.00005184539],"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":[0.0001689946,0.00009377821,0.1424334,0.0001116355,0.0003809848,0.0004103336,0.0004677741,0.6874257,0.02412316,0.04381989,0.001020053,0.09954425],"study_design_scores_gemma":[0.000005400829,0.00003458464,0.07806236,0.00001159252,0.00003323271,0.00003661876,0.00005117922,0.9153841,0.002352468,0.003341708,0.0006657187,0.0000210407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7387069,0.0003529305,0.2563994,0.0002137368,0.00003602563,0.00002704478,0.0002943648,0.0001933926,0.003776264],"genre_scores_gemma":[0.9853317,0.0001145514,0.01386999,0.00000893728,0.000008068863,0.000006226637,0.0001692576,0.00002075176,0.0004704346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01854719,"threshold_uncertainty_score":0.03687853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419216737471965,"score_gpt":0.2536384980236135,"score_spread":0.2094463306488938,"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."}}