{"id":"W1519613664","doi":"10.1002/2013jg002446","title":"Comparison of terrestrial evapotranspiration estimates using the mass transfer and Penman‐Monteith equations in land surface models","year":2013,"lang":"en","type":"article","venue":"Journal of Geophysical Research Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Postdoctoral Science Foundation; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Evapotranspiration; Penman–Monteith equation; Biome; Eddy covariance; Environmental science; Leaf area index; Richards equation; Atmospheric sciences; Canopy; Meteorology; Mathematics; Hydrology (agriculture); Ecology; Ecosystem; Soil science; Soil water; Geography; Geology","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":[0.0009838218,0.00006431476,0.0001568862,0.00007691274,0.0001558081,0.00009463394,0.0002622203,0.00003581446,0.00002698655],"category_scores_gemma":[0.00007010119,0.00003690687,0.00003867904,0.0004726479,0.0005798705,0.0006642357,0.00003618417,0.0002418705,0.000003064801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004076591,"about_ca_system_score_gemma":0.00004234228,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01425166,"about_ca_topic_score_gemma":0.0007575821,"domain_scores_codex":[0.9984316,0.0001476839,0.0003228908,0.0001136857,0.0007762537,0.0002079248],"domain_scores_gemma":[0.9993365,0.0004048768,0.00006987225,0.00007528521,0.00004035225,0.00007313546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004859835,0.0002104558,0.6369221,0.000007299829,0.000007904483,0.00000245556,0.001324568,0.156798,0.2024721,0.0004901756,0.000007249577,0.001709112],"study_design_scores_gemma":[0.0001879021,0.000189557,0.08776359,0.00002241931,0.00000685597,0.000002082761,0.0001539469,0.9022524,0.00116843,0.008203212,0.000005681444,0.00004391411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895899,0.00004754987,0.009664354,0.0003678756,0.00003300592,0.0001675816,0.000005911055,0.000001398416,0.0001224604],"genre_scores_gemma":[0.9986941,0.00002367443,0.001243453,0.000004096901,0.0000180396,0.000001694221,7.701613e-7,0.000002486137,0.00001165646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7454544,"threshold_uncertainty_score":0.9923126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1013454857411023,"score_gpt":0.3567644561382757,"score_spread":0.2554189703971734,"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."}}