{"id":"W4411540157","doi":"10.1016/j.jhydrol.2025.133754","title":"Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; European Space Agency; Natural Resources Canada; National Aeronautics and Space Administration","keywords":"Water equivalent; Snow; Water storage; Environmental science; Hydrology (agriculture); Geology; Soil science; Geomorphology; Geotechnical engineering","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":[0.0002811414,0.000434499,0.0002748928,0.0009444373,0.0001853964,0.0004374684,0.0004065531,0.0001994838,0.0003052517],"category_scores_gemma":[0.0004512837,0.0001722638,0.0004812089,0.0008340155,0.0001743873,0.0004746755,0.0003901171,0.0002496944,0.0001299899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004641577,"about_ca_system_score_gemma":0.0008489956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04022482,"about_ca_topic_score_gemma":0.05190469,"domain_scores_codex":[0.9998369,0.00002274292,0.000006544329,0.00003613879,0.00006970061,0.00002786787],"domain_scores_gemma":[0.9998872,0.00001969661,0.00002243874,0.00001378222,0.00004713017,0.000009840413],"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":[0.0003001449,0.0001730758,0.05833712,0.0001089759,0.0002680689,0.0002992033,0.0001811998,0.6128933,0.07816171,0.001627039,0.001978247,0.245672],"study_design_scores_gemma":[0.00000914366,0.00001583077,0.01690112,0.000003073103,0.00002357968,0.00002366098,0.00002836078,0.9789535,0.003380791,0.0002817608,0.00036467,0.00001446095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7718156,0.0002254047,0.2240615,0.00008646397,0.0000196099,0.00004791241,0.0008065195,0.0008763853,0.002060815],"genre_scores_gemma":[0.9488456,0.0000914543,0.04949693,0.00001781398,0.0000133762,0.00002325148,0.001021456,0.00003668144,0.0004534826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04022482,"threshold_uncertainty_score":0.07998139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02863914098403898,"score_gpt":0.2445022587533905,"score_spread":0.2158631177693515,"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."}}