{"id":"W3020869288","doi":"10.1029/2020ms002159","title":"Joint Modeling of Crop and Irrigation in the central United States Using the Noah‐MP Land Surface Model","year":2020,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Irrigation; Environmental science; Crop yield; Crop; Sowing; Yield (engineering); Crop simulation model; Agricultural engineering; Agronomy; Engineering; Physics","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":[0.0003662305,0.0004933425,0.0004066781,0.0003039403,0.0005486772,0.0008099855,0.0007536354,0.0007553786,0.000865064],"category_scores_gemma":[0.000723769,0.000363382,0.0006024985,0.0006951268,0.0003945594,0.0005120082,0.0004599664,0.0005535912,0.0001274172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001526557,"about_ca_system_score_gemma":0.001676389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.242698,"about_ca_topic_score_gemma":0.1319217,"domain_scores_codex":[0.9998636,0.00003791176,0.000006715669,0.00004256509,0.00002344258,0.00002577096],"domain_scores_gemma":[0.999719,0.00009863299,0.00003658164,0.00002983165,0.00008764439,0.00002842118],"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":[0.00003787965,0.00003291119,0.007105183,0.000007792049,0.00003454937,0.00004372642,0.00001627051,0.9901805,0.000372469,0.0003847167,0.000453877,0.001330042],"study_design_scores_gemma":[0.00001607978,0.00001214927,0.003178986,0.00000162674,0.000009366375,0.000003084479,0.00002045268,0.9962202,0.0001434096,0.0001487228,0.0002410056,0.000004882013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887934,0.0001210406,0.004551961,0.0003187453,0.00002798184,0.00002588667,0.001331765,0.0001701546,0.004659031],"genre_scores_gemma":[0.997327,0.00004060882,0.001461612,0.0000307235,0.000003848638,0.00002203931,0.0005180855,0.00001051419,0.0005856145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.242698,"threshold_uncertainty_score":0.4825706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09794253434911741,"score_gpt":0.276699869453283,"score_spread":0.1787573351041656,"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."}}