{"id":"W2339819852","doi":"10.1088/1742-2132/13/3/259","title":"Groundwater storage change detection using micro-gravimetric technology","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysics and Engineering","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Groundwater; Gravimeter; Environmental science; Geodetic datum; Water table; Hydrology (agriculture); Data assimilation; Gravimetric analysis; Climate change; Aquifer; Water storage; Soil science; Geology; Geodesy; Meteorology; Geography; Geomorphology; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002607621,0.0003196074,0.0002776567,0.00135957,0.0001499101,0.0003924176,0.0004992002,0.0002934375,0.0006482376],"category_scores_gemma":[0.0003510686,0.0001898153,0.0001833363,0.001173494,0.0002164753,0.0006108572,0.0003580683,0.0002148033,0.0002206893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204913,"about_ca_system_score_gemma":0.000222086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0039651,"about_ca_topic_score_gemma":0.01860977,"domain_scores_codex":[0.9998001,0.00002432607,0.000009542191,0.00005035621,0.00009771064,0.00001797528],"domain_scores_gemma":[0.9997852,0.00004878654,0.00005402197,0.0000259359,0.00007378691,0.0000123173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001661891,0.00008706634,0.1615057,0.0003820235,0.0001172854,0.0002363163,0.0004525736,0.009823238,0.4847729,0.00134647,0.001679134,0.3394311],"study_design_scores_gemma":[0.00004229231,0.0005068206,0.4621647,0.00008087028,0.0002366557,0.001206774,0.001170409,0.1763989,0.3359635,0.002836284,0.01921227,0.0001804624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7374953,0.004048338,0.2467251,0.0004783068,0.0001640235,0.0000964217,0.001717096,0.002170007,0.007105386],"genre_scores_gemma":[0.9091683,0.001016776,0.08767397,0.0001172553,0.00005919133,0.00002835974,0.000339593,0.00003478356,0.001561752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0039651,"threshold_uncertainty_score":0.007884085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036368592486528,"score_gpt":0.1875368718981433,"score_spread":0.167173185973278,"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."}}