{"id":"W2999532956","doi":"10.1080/02626667.2020.1715982","title":"A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies","year":2020,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Saskatchewan; Global Institute for Water Security","funders":"","keywords":"Equifinality; Streamflow; Evapotranspiration; Environmental science; Hydrograph; Calibration; Water content; Hydrological modelling; Hydrology (agriculture); Soil science; Drainage basin; Climatology; Geology; Computer science; Mathematics; Geography; Statistics; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001225535,0.0007585085,0.000496363,0.0006723666,0.0003231255,0.0005032542,0.0005506415,0.0006176741,0.0007805155],"category_scores_gemma":[0.001253898,0.0004356239,0.0004412628,0.0004861931,0.0002408033,0.0005020442,0.000507631,0.0005174766,0.00005532376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006384247,"about_ca_system_score_gemma":0.0007599171,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01648283,"about_ca_topic_score_gemma":0.01693002,"domain_scores_codex":[0.9997754,0.0001081289,0.00001050403,0.00004620072,0.00003506296,0.00002485611],"domain_scores_gemma":[0.9996305,0.0001871281,0.00005981573,0.00002939947,0.00007967635,0.00001341733],"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":[0.000009267467,0.00002854164,0.0008152323,0.00000777906,0.0000179767,0.00001176604,0.00001380731,0.9923725,0.0009914022,0.0001607839,0.00003542437,0.005535491],"study_design_scores_gemma":[0.000003197005,0.0000092012,0.0004753629,0.000001199545,0.000002391263,0.000001470955,0.000004234527,0.999113,0.0002881258,0.00006416343,0.00003518794,0.000002479702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.608326,0.0001394528,0.3875937,0.0001290631,0.00001525683,0.0001086116,0.0001836473,0.0003798271,0.003124544],"genre_scores_gemma":[0.9544089,0.00003037636,0.04499801,0.00001870798,0.000002913958,0.0000539777,0.00007628158,0.00001922233,0.0003915002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9835172,"threshold_uncertainty_score":0.03277379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08987095749611294,"score_gpt":0.2995842969618067,"score_spread":0.2097133394656938,"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."}}