{"id":"W4296204817","doi":"10.1029/2021wr031058","title":"Conjunctive Water Management for Agriculture With Groundwater Salinity","year":2022,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Groundwater recharge; Groundwater; Conjunctive use; Environmental science; Aquifer; Hydrology (agriculture); Surface water; Water resource management; Waterlogging (archaeology); Arid; Soil salinity control; Soil salinity; Groundwater model; Water resources; Environmental engineering; Geology; Soil science; Soil water; Leaching model; Wetland; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009718218,0.0002402647,0.0002043914,0.0004013886,0.0008743344,0.0002753961,0.0005286303,0.0000519697,0.0006860437],"category_scores_gemma":[0.00000169754,0.0001287044,0.00007657571,0.0002660415,0.00009625029,0.000165283,0.0006141209,0.0004435154,0.0001240249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002033685,"about_ca_system_score_gemma":0.000001573844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003592168,"about_ca_topic_score_gemma":0.00001710109,"domain_scores_codex":[0.9972844,0.0001900933,0.0002319882,0.0004232028,0.000873928,0.0009964171],"domain_scores_gemma":[0.9993888,0.00002993818,0.00001240514,0.0003540133,0.000112207,0.000102597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002017286,0.0005143096,0.00100915,0.00140552,0.001429232,0.0002572303,0.0508323,0.8725548,0.01058108,0.0006206809,0.05524635,0.003532062],"study_design_scores_gemma":[0.001343011,0.0004840906,0.0001780048,0.0000148085,0.00004082525,0.000008329921,0.002771529,0.006918709,0.02366246,0.0005675921,0.9636309,0.0003798088],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.947173,0.0001005122,0.004068859,0.000792129,0.000237884,0.002828439,0.00003160263,0.0005659887,0.04420159],"genre_scores_gemma":[0.9690155,0.00001214859,0.0005856751,0.00007466638,0.0001816101,0.001654775,0.000507745,0.00009566888,0.02787224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9083845,"threshold_uncertainty_score":0.7511697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452362146257526,"score_gpt":0.2464824424931715,"score_spread":0.2219588210305962,"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."}}