{"id":"W4367838408","doi":"10.1029/2022wr033494","title":"An Upscaled Model of Fill‐And‐Spill Hydrological Response","year":2023,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Global Water Futures; Natural Sciences and Engineering Research Council of Canada; ArcticNet","keywords":"Surface runoff; Wetland; Environmental science; Hydrology (agriculture); Snowmelt; Runoff model; Terrain; Hydrological modelling; Probabilistic logic; Geology; Ecology; Geotechnical engineering; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003065872,0.0004084827,0.0005598633,0.0003274925,0.0004289733,0.0008028647,0.001186129,0.001023041,0.002250145],"category_scores_gemma":[0.0007212386,0.000443422,0.0006346478,0.0004109087,0.0005303124,0.000806321,0.0006100516,0.0006311607,0.0001919762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231932,"about_ca_system_score_gemma":0.001198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03792298,"about_ca_topic_score_gemma":0.01718106,"domain_scores_codex":[0.9998648,0.00003196222,0.000006055883,0.0000481302,0.00002415601,0.00002484731],"domain_scores_gemma":[0.9997755,0.00008702616,0.00003833289,0.0000177935,0.00005574839,0.00002557493],"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":[0.000005721545,0.000009329666,0.0002464716,0.000002977774,0.000003504659,0.0000171287,0.000007516139,0.998031,0.0003158388,0.0006256996,0.00004156493,0.0006933903],"study_design_scores_gemma":[0.000001530992,0.000001991291,0.00005305991,1.991347e-7,8.193522e-7,0.000001064037,0.00000100723,0.9998023,0.00002321715,0.00008940135,0.00002468059,7.359665e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6858285,0.0001837943,0.297488,0.0004201396,0.00005579288,0.0001082454,0.0007967366,0.0008999663,0.01421889],"genre_scores_gemma":[0.9865345,0.00007025568,0.008397429,0.00003047171,0.000009672827,0.0000674728,0.0001748101,0.00003724361,0.004678114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03792298,"threshold_uncertainty_score":0.07540447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06465984645157799,"score_gpt":0.3275262667196328,"score_spread":0.2628664202680548,"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."}}