{"id":"W2127883379","doi":"10.1007/s10040-011-0724-3","title":"Groundwater storage variability and annual recharge using well-hydrograph and GRACE satellite data","year":2011,"lang":"en","type":"article","venue":"Hydrogeology Journal","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; Simon Fraser University","funders":"U.S. Geological Survey; British Geological Survey; National Center for Atmospheric Research; National Science Foundation","keywords":"Groundwater recharge; Hydrogeology; Water storage; Hydrology (agriculture); Data assimilation; Groundwater; Water table; Environmental science; Hydrograph; Water resources; Groundwater discharge; Anomaly (physics); Vadose zone; Aquifer; Climatology; Geology; Soil science; Soil water; Meteorology; Drainage basin; Inlet; Geography; Geomorphology","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":[0.001944481,0.0001664505,0.0002225867,0.00009290381,0.0003699439,0.00006465621,0.000291277,0.0001103213,0.0007237265],"category_scores_gemma":[0.00003215591,0.0001344675,0.00003212969,0.000114027,0.0002841853,0.0005952488,0.00008137694,0.0003558451,0.00003136689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003078342,"about_ca_system_score_gemma":0.00003313557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001235473,"about_ca_topic_score_gemma":0.0003938406,"domain_scores_codex":[0.9984379,0.0003321287,0.000261068,0.0003812707,0.0001900903,0.000397503],"domain_scores_gemma":[0.9991663,0.0000559833,0.0001378683,0.0003316329,0.00005676167,0.0002514579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004513025,0.00004172832,0.9924101,0.000009307947,0.00005443727,0.0000608228,0.0005064492,0.000008324635,0.00006251685,0.00004118345,0.00003761274,0.006722334],"study_design_scores_gemma":[0.0004971603,0.0002235067,0.9708583,0.00001533372,0.00008958926,0.001538339,0.000149499,0.004820474,0.00005008915,0.01840435,0.003096035,0.0002573578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975113,0.0009494452,0.0002701501,0.0000753826,0.0003532484,0.00009072364,0.00006233127,0.00001281551,0.0006746465],"genre_scores_gemma":[0.9969801,0.0003419111,0.002375491,0.0001251278,0.00009352424,1.466193e-7,0.00004635184,0.000004256525,0.00003311196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02155188,"threshold_uncertainty_score":0.7924297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06410297227588559,"score_gpt":0.2385745270196536,"score_spread":0.174471554743768,"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."}}