{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00170797,0.0004810879,0.0004815949,0.0003364623,0.0002094295,0.0008336385,0.0007118366,0.0004825272,0.0006171871],"category_scores_gemma":[0.002513619,0.0002104241,0.0005109702,0.00103848,0.0003919375,0.001220874,0.0007405625,0.0007777109,0.0002682068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003883399,"about_ca_system_score_gemma":0.001061413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01769396,"about_ca_topic_score_gemma":0.01470591,"domain_scores_codex":[0.9996423,0.0001059091,0.00003156133,0.00006743074,0.0001201163,0.00003265574],"domain_scores_gemma":[0.9990761,0.0001729861,0.00008171518,0.0003204711,0.00028449,0.00006418559],"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":[0.000354092,0.0002721074,0.07432289,0.0002718091,0.0004667871,0.0001302357,0.0002006545,0.5689393,0.03486953,0.01272963,0.01969178,0.2877511],"study_design_scores_gemma":[0.00008695698,0.0000790478,0.0269754,0.00004067476,0.00005788199,0.00002501029,0.0001121589,0.9465578,0.007508919,0.006342736,0.01215125,0.00006222245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7423365,0.003331282,0.2148216,0.00927967,0.0006697652,0.0001428195,0.01145934,0.006785872,0.01117317],"genre_scores_gemma":[0.9384502,0.001093853,0.05347329,0.0004709903,0.0001640406,0.00007834712,0.005178098,0.000326522,0.0007648089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01769396,"threshold_uncertainty_score":0.03518194,"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."}}