{"id":"W3023820845","doi":"10.2166/nh.2020.109","title":"Improved modelling of a Prairie catchment using a progressive two-stage calibration strategy with in situ soil moisture and streamflow data","year":2020,"lang":"en","type":"article","venue":"Hydrology research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Streamflow; Environmental science; Water content; Calibration; Hydrology (agriculture); Moisture; Stage (stratigraphy); Hydrological modelling; Drainage basin; Soil science; Climatology; Meteorology; Geology; Geography; Cartography; Mathematics","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":[0.001238903,0.0006562534,0.0004575664,0.0003757044,0.0003962345,0.00066563,0.001135445,0.0009042658,0.0007108143],"category_scores_gemma":[0.001954147,0.0005645765,0.0005694751,0.000434998,0.0003376374,0.0007581092,0.0007833111,0.0008109728,0.00007599783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006296329,"about_ca_system_score_gemma":0.001142416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02357321,"about_ca_topic_score_gemma":0.01668032,"domain_scores_codex":[0.9997239,0.0001100741,0.00001597657,0.00006863657,0.00004199297,0.00003945574],"domain_scores_gemma":[0.999317,0.0003235484,0.00007629231,0.00009525195,0.0001430023,0.00004496454],"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":[0.00002084171,0.00006846788,0.003460539,0.000007893681,0.00001725958,0.00002921992,0.00003241728,0.9899636,0.002337844,0.000219396,0.00004023135,0.003802228],"study_design_scores_gemma":[0.000006619435,0.000009616876,0.0007262949,9.54063e-7,0.000002308345,0.000002448911,0.000003282516,0.9986367,0.0005125466,0.00006243553,0.00003313704,0.000003666699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8950061,0.00004492174,0.1029714,0.000107211,0.00001142667,0.00008274982,0.0001275586,0.0003795558,0.00126899],"genre_scores_gemma":[0.9819772,0.00001516972,0.01768366,0.00001423163,0.000002587031,0.00002820035,0.00007821988,0.00001406694,0.00018673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02357321,"threshold_uncertainty_score":0.04687202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1246098509773684,"score_gpt":0.3422768182628646,"score_spread":0.2176669672854962,"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."}}