{"id":"W4283579720","doi":"10.1029/2021ms002855","title":"Identification and Regionalization of Streamflow Routing Parameters Using Machine Learning for the HLM Hydrological Model in Iowa","year":2022,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Manitoba","funders":"Mid-America Transportation Center, University of Nebraska-Lincoln","keywords":"Routing (electronic design automation); Streamflow; Computer science; Benchmark (surveying); Interpolation (computer graphics); Flood control; Random forest; Flood forecasting; Hydrology (agriculture); Environmental science; Meteorology; Flood myth; Machine learning; Artificial intelligence; Geology; Geography; Cartography; Drainage basin","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":[0.0007648715,0.0003272306,0.0003050923,0.0003473471,0.0002773291,0.0004894945,0.0005993522,0.0005767568,0.0005510542],"category_scores_gemma":[0.002620224,0.0002735954,0.0004467992,0.000293926,0.0003189919,0.0005004756,0.0003936189,0.0005135826,0.00006906793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112391,"about_ca_system_score_gemma":0.0009257341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07837921,"about_ca_topic_score_gemma":0.07477491,"domain_scores_codex":[0.9998353,0.00006416066,0.00000807761,0.00005217976,0.00001553052,0.00002479764],"domain_scores_gemma":[0.9992207,0.0004435903,0.0000923572,0.00007849466,0.0001302119,0.00003463854],"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":[0.0000354056,0.00003471854,0.01921507,0.000005747397,0.00002029436,0.0000302291,0.00002150182,0.9760979,0.000901571,0.0003115156,0.0001195293,0.003206506],"study_design_scores_gemma":[0.000009095094,0.000008063504,0.002371264,0.000001475498,0.000004292553,0.00000274996,0.00001321229,0.99717,0.000265509,0.0001089495,0.00004162717,0.000003810397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830031,0.00001953238,0.01581182,0.00009515441,0.000005076158,0.00001779733,0.0002457745,0.0001115334,0.000690335],"genre_scores_gemma":[0.9941937,0.000007842006,0.005465867,0.000007983637,0.00000197342,0.00001555502,0.0001820914,0.000008192856,0.0001167376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07837921,"threshold_uncertainty_score":0.155846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.032007880883637,"score_gpt":0.2682017975167372,"score_spread":0.2361939166331002,"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."}}