{"id":"W3036321349","doi":"10.1016/j.scitotenv.2020.140120","title":"Erratum to “A new approach for generating optimal GLDAS hydrological products and uncertainties” [Sci. Total Environ. Volume 730, 15 August 2020, 138932]","year":2020,"lang":"en","type":"erratum","venue":"The Science of The Total Environment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Volume (thermodynamics); Environmental science; Data assimilation; Meteorology; Geography; Physics; Thermodynamics","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":[0.00265583,0.001659795,0.0008952564,0.002750082,0.002377121,0.003300563,0.001553911,0.002893538,0.1636239],"category_scores_gemma":[0.02025299,0.0007070572,0.0007965257,0.002278987,0.0007853152,0.002374862,0.001473521,0.003733438,0.1087115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002450109,"about_ca_system_score_gemma":0.004656215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02940691,"about_ca_topic_score_gemma":0.04580835,"domain_scores_codex":[0.9983607,0.0002546103,0.0001995789,0.0001726599,0.0009266418,0.00008574226],"domain_scores_gemma":[0.988502,0.001996683,0.0004255337,0.0005331532,0.008188793,0.0003537482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001464552,0.000003403556,0.00003286648,0.0000217689,0.000001757617,0.00002391479,0.000004027213,0.0001103887,0.00002431931,0.0005992459,0.9947524,0.004411268],"study_design_scores_gemma":[0.00002649558,0.00001283151,0.0003806216,0.0001215384,0.000008240962,0.00004572271,0.0000311016,0.0005440923,0.0002174958,0.001475615,0.9971153,0.00002101],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.001058606,0.002073441,0.01086582,0.07717539,0.7268775,0.0002381383,0.04846931,0.003738617,0.1295032],"genre_scores_gemma":[0.01007874,0.004302597,0.03178543,0.04152025,0.04193568,0.0005089276,0.05425669,0.005983503,0.8096282],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1636239,"threshold_uncertainty_score":0.5473765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01556317157405363,"score_gpt":0.2102619091216915,"score_spread":0.1946987375476379,"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."}}