{"id":"W2924122332","doi":"10.1016/j.agrformet.2019.02.037","title":"Simulation of maize evapotranspiration: An inter-comparison among 29 maize models","year":2019,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Nutrasource","funders":"Bundesministerium für Bildung und Forschung; Foundation for Food and Agriculture Research","keywords":"Evapotranspiration; Environmental science; Crop simulation model; Biometeorology; Eddy covariance; Phenology; Standard deviation; Statistics; Simulation modeling; Mean squared error; Crop; Mathematics; Canopy; Agronomy; Ecology; Geography; Ecosystem; Forestry","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":[0.00008658758,0.0001079414,0.0001758731,0.00002047562,0.00004323452,0.00001369382,0.00009873583,0.0001082976,0.000190806],"category_scores_gemma":[0.000003344985,0.00006800044,0.00003877674,0.00009247434,0.00008933595,0.0004586691,0.00004403393,0.00008877332,0.00003206087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001872574,"about_ca_system_score_gemma":0.000001428283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002400477,"about_ca_topic_score_gemma":0.001001561,"domain_scores_codex":[0.9992617,0.00004703772,0.0002250193,0.000212879,0.0001043744,0.0001490166],"domain_scores_gemma":[0.9996898,0.00004435984,0.000091531,0.0001065898,0.00001174219,0.00005593847],"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":[0.0000167456,0.00002450142,0.2878914,0.000003531297,0.000007419562,3.414476e-7,0.0002511047,0.707954,0.002554408,0.0007351237,0.000004491704,0.0005569318],"study_design_scores_gemma":[0.0001861966,0.0001674492,0.4347328,0.000003136927,0.00001766047,0.000004064736,0.0000253717,0.5630028,0.00005577335,0.001658513,0.00006418438,0.00008207274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950778,0.0000168297,0.001854079,0.00004813906,0.00007485442,0.0001873161,0.000008719612,0.00001957251,0.002712699],"genre_scores_gemma":[0.9991964,0.000005695551,0.0003259345,0.00002867095,0.00001573869,0.000006492922,0.00008928723,0.000003847416,0.0003280026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1468414,"threshold_uncertainty_score":0.2772977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059952255141759,"score_gpt":0.2102056750862801,"score_spread":0.1996061525348625,"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."}}