{"id":"W2146698538","doi":"10.1175/mwr-d-13-00011.1","title":"Parallel Implementation of an 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":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Ensemble Kalman filter; Scaling; Computer science; Data assimilation; Grid; Fraction (chemistry); Interpolation (computer graphics); Kalman filter; Algorithm; Filter (signal processing); Meteorology; Mathematics; Motion (physics); Extended Kalman filter; Artificial intelligence; Physics; Geometry; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001955647,0.00008326634,0.0001983295,0.00001971949,0.00004628182,0.00001447289,0.0001266848,0.00002282502,0.04201873],"category_scores_gemma":[0.00001018198,0.00005475364,0.00005856351,0.00009104744,0.00002109781,0.0002155067,0.0000049473,0.00003999098,0.0005712596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001364283,"about_ca_system_score_gemma":0.000008125196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001511511,"about_ca_topic_score_gemma":0.0006211481,"domain_scores_codex":[0.9992027,0.0001129032,0.0002711839,0.0001411809,0.0001246487,0.0001474331],"domain_scores_gemma":[0.9995623,0.00004311298,0.00008452603,0.0001959442,0.00003205869,0.00008207822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006119779,0.00005152503,0.1285614,0.0003855837,0.00003346765,0.000002250004,0.0002568366,0.00167644,0.00009435747,0.0003566662,0.004255478,0.8643199],"study_design_scores_gemma":[0.0003123368,0.0003779538,0.9382983,0.0001335588,0.00005277709,0.000001105665,0.00009245954,0.003354997,0.00003031574,0.007500827,0.04963283,0.0002124654],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8964438,0.05960134,0.000162572,0.0009673213,0.00008544511,0.001321845,0.0000647207,0.00004523402,0.04130771],"genre_scores_gemma":[0.9932854,0.00203517,0.002613401,0.001550062,0.00003329213,0.00001377223,0.0001781992,0.000003024273,0.0002877059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8641074,"threshold_uncertainty_score":0.958857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03753217551105863,"score_gpt":0.2802973843423987,"score_spread":0.2427652088313401,"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."}}