{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008040755,0.0006873066,0.000693276,0.0002585319,0.0003289146,0.0009511943,0.001124013,0.0007264861,0.002318329],"category_scores_gemma":[0.001582584,0.0004849933,0.0006152323,0.0004427905,0.0007610879,0.001107947,0.001228589,0.0008428262,0.000127628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408373,"about_ca_system_score_gemma":0.001355851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0137023,"about_ca_topic_score_gemma":0.01714746,"domain_scores_codex":[0.9993062,0.0002141936,0.00002745522,0.00016456,0.0001141402,0.000173563],"domain_scores_gemma":[0.999361,0.0002613818,0.0001720135,0.0000464597,0.00009877321,0.00006038202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004395243,0.0000603775,0.0008325935,0.00002588699,0.00002339355,0.00005728275,0.00002580796,0.9861347,0.002125661,0.003615804,0.000300538,0.006753959],"study_design_scores_gemma":[0.00001082752,0.0001010001,0.0006031975,0.000002721046,0.00001132207,0.000008059373,0.00002104649,0.9958497,0.0005223004,0.002428391,0.0004352406,0.000006153143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4216751,0.0002778358,0.5592649,0.0008311644,0.00007037615,0.0002568044,0.0002880618,0.0003152663,0.01702045],"genre_scores_gemma":[0.9907361,0.00004664897,0.007355944,0.00002489561,0.000006036172,0.00003731914,0.00002939124,0.00001166917,0.001751972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0137023,"threshold_uncertainty_score":0.0272451,"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."}}