{"id":"W3138529858","doi":"10.1029/2020wr028654","title":"Integrating Gravimetry Data With Thermal Infra‐Red Data From Satellites to Improve Efficiency of Operational Irrigation Advisory in South Asia","year":2021,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"National Science Foundation","keywords":"Environmental science; Irrigation; Evapotranspiration; Groundwater; Water resource management; Hydrology (agriculture); Remote sensing; Geography; Engineering","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":[0.001662759,0.0001195277,0.0001789988,0.0001668809,0.0001605659,0.0002079388,0.00118489,0.00005229422,0.0006024078],"category_scores_gemma":[0.0002111243,0.00007628147,0.00001484604,0.0005368647,0.0001041881,0.0003444382,0.0004270312,0.0003373169,0.0001238793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006538437,"about_ca_system_score_gemma":0.0001048169,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007968023,"about_ca_topic_score_gemma":0.004492943,"domain_scores_codex":[0.9972057,0.0003828526,0.0002881983,0.0006331584,0.001086109,0.000404028],"domain_scores_gemma":[0.9984334,0.0001559345,0.00003745799,0.001034751,0.0002246752,0.0001137748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002605705,0.0001283941,0.8562882,0.00004894925,0.00005049492,0.00003484623,0.008923667,0.001708318,0.11088,0.00001389716,0.00006353006,0.02159914],"study_design_scores_gemma":[0.0007607263,0.0003367903,0.8962165,0.0001744557,0.00001326665,0.000001458241,0.004644538,0.03021227,0.06402221,0.0007297897,0.002591162,0.0002968565],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965529,0.0001870936,0.00005494432,0.0002358603,0.000041273,0.0002412755,0.001060229,0.000006935155,0.00161943],"genre_scores_gemma":[0.9912924,0.000002265928,0.001935657,0.00003384581,0.00008830655,0.000001878219,0.006471509,0.000005682132,0.0001684512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04685776,"threshold_uncertainty_score":0.998638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0847148361880418,"score_gpt":0.3044443554478899,"score_spread":0.2197295192598481,"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."}}