{"id":"W2730032521","doi":"10.1002/hyp.11267","title":"Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach","year":2017,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Global Institute for Water Security; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Economic Affairs; Canada Research Chairs","keywords":"Streamflow; Hydrograph; Calibration; Sensitivity (control systems); Hydrological modelling; Computer science; Watershed; Environmental science; Range (aeronautics); Drainage basin; Climatology; Statistics; Mathematics; Geology; Machine learning","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":[0.002194804,0.0008921683,0.0008044097,0.001042087,0.0004704162,0.0009905385,0.0006392606,0.001137276,0.0006555258],"category_scores_gemma":[0.003461749,0.0007186534,0.001114499,0.0004166001,0.0006840318,0.0006262273,0.001102089,0.0007543094,0.00006071321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001237642,"about_ca_system_score_gemma":0.001089386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01664983,"about_ca_topic_score_gemma":0.007142549,"domain_scores_codex":[0.9993168,0.0004416472,0.00002594883,0.00008532866,0.00007461203,0.00005565669],"domain_scores_gemma":[0.9984776,0.00117772,0.0001160431,0.00005658111,0.0001464682,0.00002548621],"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":[0.0000115698,0.000009924624,0.0002795645,0.000006322666,0.0000183528,0.00001844471,0.000007212162,0.9974067,0.0005325026,0.0004592117,0.00002022825,0.001229995],"study_design_scores_gemma":[0.000001302326,0.000005588421,0.0001038902,0.000001077888,0.000002841513,0.000001442479,0.000002439999,0.999501,0.0001997438,0.0001632103,0.00001545126,0.000002040738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3512704,0.0001856113,0.6437201,0.0003582618,0.00001849349,0.0001210856,0.0001460198,0.0003490211,0.003830931],"genre_scores_gemma":[0.9700053,0.00004030143,0.02918727,0.00003176391,0.000007594799,0.00008450359,0.00005700028,0.00002430048,0.0005618994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01664983,"threshold_uncertainty_score":0.03310579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06944902562592142,"score_gpt":0.2682125737311577,"score_spread":0.1987635481052363,"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."}}