{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000167143,0.0001772116,0.0001545222,0.00007455012,0.0003383606,0.0003600658,0.0002516601,0.00006255562,0.00003577114],"category_scores_gemma":[0.000003526999,0.0001135203,0.0000505681,0.00001252726,0.00002761438,0.0006349686,0.0001709067,0.0001014841,0.0000817329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000437075,"about_ca_system_score_gemma":0.000001902216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007293638,"about_ca_topic_score_gemma":0.00001267404,"domain_scores_codex":[0.999045,0.000008697859,0.0001597839,0.0002347869,0.0001461265,0.0004055631],"domain_scores_gemma":[0.9993629,7.507697e-7,0.00001248659,0.000540623,0.00003731166,0.00004591858],"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":[0.000009730673,0.00001041145,0.00009109332,0.00004831686,0.00002665006,0.000003443644,0.0008186278,0.9543236,0.04453296,0.000007023717,0.00002163727,0.0001065054],"study_design_scores_gemma":[0.0002442618,0.000004145969,0.000008648602,0.00001305246,0.00002742081,0.000001041516,0.000005196977,0.9448664,0.05401214,0.000326019,0.0002782241,0.0002135126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7830307,0.000008997019,0.2149082,0.000103819,0.0001828184,0.0001689549,9.763199e-7,0.0001922873,0.001403295],"genre_scores_gemma":[0.9954424,0.000004305825,0.002770589,0.00001488769,0.0001752657,0.00001179596,0.00002213955,0.00005799279,0.00150065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2124117,"threshold_uncertainty_score":0.4629224,"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."}}