{"id":"W2965378851","doi":"10.1029/2019gl083525","title":"The Groundwater Recovery Paradox in South India","year":2019,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Water resources management and optimization","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Groundwater; Environmental science; Water resource management; Odds; Groundwater resources; Resource (disambiguation); Hydrology (agriculture); Aquifer; Geology; Statistics; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003489664,0.00008868811,0.00009025905,0.0001337141,0.00007361238,0.0001905125,0.0002797553,0.00003140671,0.00002967244],"category_scores_gemma":[0.00001859979,0.00006276599,0.00004218312,0.0003492898,0.00005911,0.0001618267,0.00009893566,0.0003736144,0.001399479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007456099,"about_ca_system_score_gemma":0.000003461529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003536696,"about_ca_topic_score_gemma":0.000006168455,"domain_scores_codex":[0.9987037,0.00008658026,0.0001201138,0.000161637,0.0003971436,0.0005308553],"domain_scores_gemma":[0.9995189,0.000157283,0.000008449096,0.0002567522,0.00001392333,0.00004472419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008463671,0.0003641454,0.0716396,0.0007640076,0.0005113205,0.0002605016,0.01724811,0.6852431,0.05492608,0.00741805,0.09386306,0.06691565],"study_design_scores_gemma":[0.003975135,0.0005821412,0.4591882,0.0002673763,0.00002171228,0.000001662257,0.001614709,0.2910934,0.004554687,0.00743005,0.229541,0.001729862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949986,0.00002594143,0.0002089094,0.0007920776,0.0001601883,0.0003009497,6.73083e-7,0.00006012018,0.003452527],"genre_scores_gemma":[0.9984317,0.00001899815,0.0000380103,0.0001458913,0.0001252534,0.00003699997,0.000008814723,0.00002279092,0.001171546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3941497,"threshold_uncertainty_score":0.999378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480098131118005,"score_gpt":0.2347188529048653,"score_spread":0.2199178715936853,"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."}}