{"id":"W2911181190","doi":"10.1016/j.ijggc.2018.10.009","title":"Dynamic characterization of geologic CO2 storage aquifers from monitoring data with ensemble Kalman filter","year":2019,"lang":"en","type":"article","venue":"International journal of greenhouse gas control","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Energi Simulation","keywords":"Ensemble Kalman filter; Data assimilation; Aquifer; Specific storage; Kalman filter; Plume; Aquifer properties; Saturation (graph theory); Dynamic data; Soil science; Uncertainty quantification; Filter (signal processing); Environmental science; Geology; Extended Kalman filter; Geotechnical engineering; Engineering; Groundwater; Computer science; Mathematics; Meteorology; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001910242,0.0003481916,0.0003266979,0.0004415654,0.0001949155,0.0004256825,0.0002653398,0.0003464362,0.0003246391],"category_scores_gemma":[0.0005635021,0.0001792661,0.0002901055,0.000498984,0.0001260941,0.0005043886,0.0002468113,0.0002411463,0.00008469773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003219297,"about_ca_system_score_gemma":0.0004678007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02069937,"about_ca_topic_score_gemma":0.02241914,"domain_scores_codex":[0.9999219,0.000008038869,0.000005350223,0.00003009189,0.00001812109,0.00001652092],"domain_scores_gemma":[0.9998124,0.00006584488,0.00002930053,0.00001846893,0.00006506417,0.000008849835],"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":[0.0005123156,0.0002041811,0.09786376,0.0001177057,0.0001763012,0.0002116329,0.0002060836,0.6265101,0.09394524,0.001091667,0.00115201,0.1780091],"study_design_scores_gemma":[0.000003539154,0.00001158222,0.01762629,0.000001929752,0.00001135622,0.000007891173,0.00001446931,0.9789982,0.00309065,0.0001262207,0.0001013667,0.000006514853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8959047,0.000188914,0.1023088,0.00006233645,0.00001556563,0.00001270212,0.0004054301,0.0002985545,0.0008029944],"genre_scores_gemma":[0.9940909,0.00005040882,0.005348386,0.000004578363,0.000004144375,0.000007383753,0.000244982,0.000008766349,0.0002406031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02069937,"threshold_uncertainty_score":0.04115772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242155445188115,"score_gpt":0.248009448240007,"score_spread":0.2355878937881259,"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."}}