{"id":"W2969940692","doi":"10.1029/2018wr024618","title":"Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges","year":2019,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":615,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"National Aeronautics and Space Administration","keywords":"Groundwater; Data assimilation; Environmental science; Precipitation; Hydrology (agriculture); Assimilation (phonology); Groundwater flow; Streamflow; Proxy (statistics); Drainage basin; Climatology; Meteorology; Geology; Aquifer; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00115065,0.00009872516,0.0001231538,0.00005323599,0.0002364769,0.000226595,0.0002969835,0.00005134989,0.00004980126],"category_scores_gemma":[0.00002101285,0.00006397702,0.00001296793,0.00007079809,0.00009031756,0.0004652823,0.0001252427,0.00009571241,0.00005581071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000442274,"about_ca_system_score_gemma":0.000006023683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007150728,"about_ca_topic_score_gemma":0.0006523695,"domain_scores_codex":[0.998409,0.0001111882,0.000108654,0.0004460151,0.0005059995,0.0004190818],"domain_scores_gemma":[0.9993995,0.00007771208,0.00001821038,0.0003236039,0.00007635777,0.000104558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001222841,0.00001976181,0.8811494,0.0002831857,0.0000224404,0.000001945655,0.001128313,0.00004677995,0.0001115415,0.00002290794,0.00007371532,0.1170177],"study_design_scores_gemma":[0.0004298907,0.0003022444,0.6850083,0.00003434329,0.000006592333,0.0000036391,0.0006747328,0.002050702,0.0002404777,0.004649691,0.3064517,0.0001476085],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873286,0.009584412,0.0000060134,0.001170283,0.0001314769,0.0003502689,0.00007401528,0.0000122069,0.001342761],"genre_scores_gemma":[0.9971071,0.001833417,0.0002056087,0.000008212515,0.0001570664,0.000003284682,0.0001310825,0.000003638674,0.0005505919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.306378,"threshold_uncertainty_score":0.2608907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1709467331816371,"score_gpt":0.3414350394402879,"score_spread":0.1704883062586508,"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."}}