{"id":"W2591148315","doi":"10.3390/w9030157","title":"Modeling Crop Water Productivity Using a Coupled SWAT–MODSIM Model","year":2017,"lang":"en","type":"article","venue":"Water","topic":"Water resources management and optimization","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Environmental science; Irrigation; Productivity; SWAT model; Soil and Water Assessment Tool; Arid; Agriculture; Crop; Yield (engineering); Crop yield; Agronomy; Plateau (mathematics); Deficit irrigation; Hydrology (agriculture); Water resource management; Irrigation management; Mathematics; Drainage basin; Geography; Ecology; Biology; Streamflow","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003688638,0.0007290415,0.0005717867,0.0004306314,0.0004717393,0.0006654455,0.001118343,0.0008627611,0.001288285],"category_scores_gemma":[0.0006720187,0.0004911955,0.001050007,0.0008595822,0.000346161,0.0006582064,0.0005652916,0.0006412211,0.0001732769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062276,"about_ca_system_score_gemma":0.001432604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08086465,"about_ca_topic_score_gemma":0.04437475,"domain_scores_codex":[0.9998409,0.00003931709,0.00001120907,0.00004213943,0.00003466143,0.0000317754],"domain_scores_gemma":[0.9997004,0.0001049611,0.00004077007,0.00002495885,0.00009516816,0.00003376092],"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":[0.00002343958,0.00002284589,0.00258733,0.00001179727,0.00003277704,0.00002287986,0.00001337588,0.9947588,0.0005681298,0.0003560701,0.0001730938,0.001429524],"study_design_scores_gemma":[0.000009308104,0.000006419925,0.0005197236,8.605417e-7,0.000006492319,0.000002385445,0.000007227491,0.9990203,0.0001408511,0.0001204306,0.0001624603,0.000003599504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265816,0.0001676978,0.0594771,0.0003483694,0.00007499522,0.0001024313,0.002839275,0.0008872057,0.009521306],"genre_scores_gemma":[0.9813844,0.00009519052,0.01535852,0.00004156009,0.00001609261,0.0001015703,0.001501038,0.00004956599,0.001451961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08086465,"threshold_uncertainty_score":0.1607879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346088077163957,"score_gpt":0.2266683886976671,"score_spread":0.1932075079260276,"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."}}