{"id":"W3090360156","doi":"10.1002/hyp.13923","title":"A modified trapezoid framework model for partitioning regional evapotranspiration","year":2020,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Science and Technology of the People's Republic of China","keywords":"Evapotranspiration; Transpiration; Eddy covariance; Moderate-resolution imaging spectroradiometer; Latent heat; Water cycle; Mean squared error; Sensible heat; Environmental science; Boundary (topology); Vegetation (pathology); Mathematics; Hydrology (agriculture); Algorithm; Atmospheric sciences; Meteorology; Statistics; Geology; Geography","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":[0.00006216364,0.00009363056,0.00009895684,0.000007371484,0.0001364082,0.00002730932,0.0001213141,0.0001113939,0.0001306113],"category_scores_gemma":[0.00009792036,0.00007500988,0.00004151354,0.0001552993,0.00007948494,0.0001586424,0.00002033286,0.0001094197,0.00003706963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001560379,"about_ca_system_score_gemma":0.00001154365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002386817,"about_ca_topic_score_gemma":0.000007820644,"domain_scores_codex":[0.9992514,0.00001221342,0.0001507379,0.0002639833,0.0001501982,0.0001714858],"domain_scores_gemma":[0.9997299,0.00007156562,0.00004392253,0.0000564134,0.00001023816,0.0000879902],"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":[0.00006448029,0.00003599877,0.000739936,0.0000222119,0.000003801877,0.000001009668,0.000336031,0.9955608,0.0007352632,0.002145879,0.00009158182,0.0002629532],"study_design_scores_gemma":[0.0001431969,0.00009417339,0.00009364048,0.00000569718,0.00001301649,0.000001879543,0.000002469613,0.9467586,0.00009372512,0.05198459,0.000703408,0.0001055986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3601762,0.00003559915,0.6349304,0.003342065,0.000009436437,0.0001983563,0.0000192244,0.000092057,0.001196677],"genre_scores_gemma":[0.9896151,0.00002426513,0.007141395,0.00297802,0.00003779885,0.00009945295,0.00005324357,0.000006866163,0.0000438252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6294389,"threshold_uncertainty_score":0.3058814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06518911357914457,"score_gpt":0.2497380862471144,"score_spread":0.1845489726679699,"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."}}