{"id":"W2343124376","doi":"10.5194/hess-20-4375-2016","title":"Combined assimilation of streamflow and snow water equivalent for mid-termensemble streamflow forecasts in snow-dominated regions","year":2016,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Data assimilation; Ensemble Kalman filter; Environmental science; Proxy (statistics); Snow; Climatology; Drainage basin; Meteorology; Kalman filter; Statistics; Geology; Geography; Mathematics; Extended Kalman filter","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002653598,0.0002802321,0.0001935196,0.000230029,0.0003027785,0.0004606652,0.0002242972,0.000227336,0.0004191096],"category_scores_gemma":[0.0005886203,0.0001224853,0.0001991241,0.0002168943,0.0001177545,0.0003029261,0.0002255496,0.0003230008,0.00009099925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008733577,"about_ca_system_score_gemma":0.001362724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3195726,"about_ca_topic_score_gemma":0.4425053,"domain_scores_codex":[0.99993,0.00001268549,0.000003554229,0.00001836321,0.00001760452,0.00001768307],"domain_scores_gemma":[0.9998083,0.00003658818,0.00001626993,0.0000192582,0.00008696662,0.00003259904],"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":[0.0008021525,0.0003111815,0.1648906,0.00004470204,0.0002059295,0.0002008509,0.0001612334,0.7370214,0.02448243,0.0003649025,0.001969211,0.06954542],"study_design_scores_gemma":[0.00001767877,0.00003265115,0.05638124,0.000004893696,0.00001565475,0.000004655933,0.00004185787,0.940282,0.002830108,0.00005456713,0.0003261059,0.000008516586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964756,0.00004748311,0.002236911,0.00005748487,0.00001220882,0.00001011121,0.0002491596,0.0001396641,0.0007713899],"genre_scores_gemma":[0.9977983,0.00001689875,0.001553218,0.000004625296,0.000002267447,0.000003155433,0.000369388,0.000004335163,0.0002478117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3195726,"threshold_uncertainty_score":0.635425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831483040824929,"score_gpt":0.2304480359445121,"score_spread":0.2121332055362628,"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."}}