{"id":"W3048420536","doi":"","title":"Streamflow Data Assimilation in SWAT Model Using Extended Kalman Filter","year":2014,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Streamflow; Flood forecasting; Ensemble Kalman filter; Environmental science; Data assimilation; Watershed; Climatology; SWAT model; Soil and Water Assessment Tool; Kalman filter; Meteorology; Extended Kalman filter; Flood myth; Mathematics; Hydrology (agriculture); Statistics; Computer science; Geography; Drainage basin; Geology","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":[0.0002691094,0.0004026912,0.0003981309,0.0002529156,0.0003032095,0.0004533058,0.0003766287,0.0004247204,0.001170307],"category_scores_gemma":[0.0005227515,0.0002128031,0.0004906518,0.0003760818,0.0001584495,0.0006265091,0.000244404,0.000377002,0.0003110144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004578923,"about_ca_system_score_gemma":0.0006979366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04878206,"about_ca_topic_score_gemma":0.03248222,"domain_scores_codex":[0.9999081,0.00001852756,0.00000811011,0.00002782056,0.00002558999,0.00001183314],"domain_scores_gemma":[0.9998486,0.00002937461,0.00001543689,0.00001508679,0.00008319207,0.000008274229],"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":[0.00004204769,0.00002761347,0.003301905,0.00002273234,0.00003649305,0.00003160362,0.00002369719,0.9743109,0.002665343,0.0006037253,0.0007391415,0.01819479],"study_design_scores_gemma":[0.000006763427,0.000007360699,0.0008724686,0.00000184681,0.000004514196,0.000002107567,0.000002592972,0.9981415,0.0004343819,0.0001472212,0.000374539,0.000004680611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5677531,0.0003426834,0.417105,0.0003569918,0.0002449763,0.0001100153,0.002947745,0.00307041,0.00806902],"genre_scores_gemma":[0.9535726,0.0001666383,0.04113694,0.00003643869,0.00003867156,0.00008134121,0.001850597,0.00007423434,0.003042508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04878206,"threshold_uncertainty_score":0.09699619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04317198382345738,"score_gpt":0.2674676031191798,"score_spread":0.2242956192957224,"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."}}