{"id":"W3123890581","doi":"10.1175/jhm-d-20-0106.1","title":"Verification of Regional Deterministic Precipitation Analysis Products Using Snow Data Assimilation for Application in Meteorological Network Assessment in Sparsely Gauged Nordic Basins","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Yukon University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Conservation Service; Environment and Climate Change Canada; U.S. Department of Agriculture","keywords":"Snowpack; Streamflow; Data assimilation; Environmental science; Snow; Precipitation; Climatology; Meteorology; Hydrological modelling; Hydrometeorology; Proxy (statistics); Drainage basin; Computer science; Geology; Geography; 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":[0.001053521,0.0003617982,0.0001989601,0.0004533789,0.0004358989,0.0006055667,0.0004766545,0.000275497,0.0003443369],"category_scores_gemma":[0.00206195,0.0001525807,0.0003613383,0.0004651098,0.000304214,0.0003871527,0.0003916016,0.0002284202,0.00005010008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367645,"about_ca_system_score_gemma":0.001991606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3088416,"about_ca_topic_score_gemma":0.3173439,"domain_scores_codex":[0.9997594,0.00006332544,0.00002101333,0.00005677225,0.00005838984,0.00004112037],"domain_scores_gemma":[0.9992177,0.0001804153,0.00009938897,0.0001213508,0.0003374397,0.00004365034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002182664,0.0001475091,0.1942936,0.00003606622,0.0001093094,0.0001409941,0.0001428287,0.7732946,0.008312153,0.0006591235,0.0004263869,0.0222192],"study_design_scores_gemma":[0.00002228104,0.00003849701,0.05727087,0.00000622703,0.00001476365,0.000007579021,0.0000817365,0.9387203,0.003491098,0.0001260805,0.0002105504,0.00001010116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994657,0.00001959343,0.004229913,0.00002033516,0.000004847845,0.00001931433,0.0004046347,0.0001173616,0.0005271121],"genre_scores_gemma":[0.9961873,0.000009913304,0.003300591,0.000003171784,8.626848e-7,0.000007712792,0.0004116251,0.000005302648,0.00007347714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3088416,"threshold_uncertainty_score":0.6140879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1469390250065865,"score_gpt":0.3252368627845663,"score_spread":0.1782978377779798,"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."}}