{"id":"W4323832803","doi":"10.1016/j.agrformet.2023.109396","title":"Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models","year":2023,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Alberta","funders":"Office of Experimental Program to Stimulate Competitive Research; Cooperative State Research, Education, and Extension Service; U.S. Department of Agriculture; Office of Science; Met Office; U.S. Department of Energy","keywords":"Evapotranspiration; Environmental science; Eddy covariance; Irrigation scheduling; Irrigation; Lysimeter; Transpiration; Simulation modeling; Hydrology (agriculture); Biometeorology; Crop simulation model; Crop coefficient; Canopy; Crop yield; Atmospheric sciences; Agronomy; Soil water; Soil science; Ecosystem; Ecology; Mathematics; Geology; Biology","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":[0.001413969,0.0007911963,0.0008129203,0.000675212,0.0005964055,0.0007456413,0.001014989,0.001330765,0.001172405],"category_scores_gemma":[0.002565962,0.000487683,0.001274531,0.0008936189,0.0005449787,0.0007144344,0.000486615,0.0007206756,0.0001317168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777444,"about_ca_system_score_gemma":0.00108194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05311497,"about_ca_topic_score_gemma":0.0257328,"domain_scores_codex":[0.9996182,0.0001610006,0.00002346331,0.00006861126,0.00004300156,0.00008567351],"domain_scores_gemma":[0.9972993,0.001949761,0.0001504652,0.0001428019,0.0003043907,0.0001532919],"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":[0.0005990457,0.0002604738,0.01025832,0.00003750066,0.0001439786,0.00004161802,0.00006717182,0.9844679,0.001241237,0.0004522302,0.0002074275,0.002223068],"study_design_scores_gemma":[0.00009584802,0.0002264718,0.008424006,0.000004479286,0.00006401438,0.00000914729,0.00006886505,0.9895889,0.001161154,0.0001889195,0.0001540852,0.00001418101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978879,0.00006176127,0.0006552917,0.00005978468,0.000009574849,0.00001341804,0.00024055,0.00003851036,0.001033304],"genre_scores_gemma":[0.9988307,0.00003957531,0.0005707517,0.00000962706,0.000003591918,0.00001465014,0.0002766956,0.00001613466,0.0002382156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05311497,"threshold_uncertainty_score":0.1056116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950370219591954,"score_gpt":0.2248956398501931,"score_spread":0.2053919376542735,"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."}}