{"id":"W2802275568","doi":"10.5194/hess-22-4685-2018","title":"Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed","year":2018,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Snowpack; Snowmelt; Environmental science; Snow; Streamflow; Watershed; Arctic; Climatology; Water year; Surface runoff; Hydrology (agriculture); Precipitation; Meteorology; Drainage basin; Geography; Geology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001203765,0.0005990555,0.000382291,0.001334742,0.0003095342,0.0008002224,0.0005951614,0.0004136137,0.001098346],"category_scores_gemma":[0.001839353,0.000149539,0.0007127315,0.002213411,0.0001506339,0.0008446247,0.0006673444,0.0004492009,0.0004705224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009504039,"about_ca_system_score_gemma":0.001402785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1008614,"about_ca_topic_score_gemma":0.1024609,"domain_scores_codex":[0.9996087,0.00009072491,0.00003350675,0.0001279168,0.00008978105,0.00004950422],"domain_scores_gemma":[0.9988746,0.0001115071,0.000110869,0.0003226614,0.000508123,0.0000722055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009293246,0.000731476,0.4520565,0.0004349389,0.00126336,0.0005604906,0.0008418302,0.3119739,0.02475181,0.002644572,0.03760489,0.1662068],"study_design_scores_gemma":[0.0001629084,0.0001296318,0.5740556,0.0001297082,0.0002817962,0.00008658643,0.0009418686,0.3586283,0.01507325,0.00124478,0.04917103,0.00009450387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9036854,0.000249977,0.01286151,0.0003114491,0.0001090064,0.000190055,0.07452279,0.002422421,0.005647443],"genre_scores_gemma":[0.8199684,0.0002152064,0.03096633,0.000085851,0.00002954617,0.0002031881,0.1470658,0.0002406755,0.001225042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1008614,"threshold_uncertainty_score":0.2005486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02683983683828985,"score_gpt":0.2793807594313014,"score_spread":0.2525409225930116,"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."}}