{"id":"W2783184691","doi":"10.1002/2017jg003978","title":"Comparison of Big‐Leaf, Two‐Big‐Leaf, and Two‐Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon‐Water Modeling","year":2018,"lang":"en","type":"article","venue":"Journal of Geophysical Research Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University Medical Centre; University of British Columbia; Queen's University; McMaster University; Environment and Climate Change Canada; University of Toronto","funders":"Canadian Space Agency; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Evapotranspiration; Eddy covariance; Canopy; Leaf area index; Transpiration; Stomatal conductance; Atmospheric sciences; Flux (metallurgy); Canopy conductance; Mathematics; Environmental science; Carbon flux; Penman–Monteith equation; Hydrology (agriculture); Botany; Ecosystem; Photosynthesis; Physics; Ecology; Chemistry; Biology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002148274,0.0001436379,0.0003220703,0.0002405103,0.0004551522,0.0001390909,0.0003431911,0.00006853489,0.00001233654],"category_scores_gemma":[0.0001818479,0.00009888421,0.00009631585,0.0005137038,0.0009078739,0.0004488839,0.000130891,0.000276972,0.000003942083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001197042,"about_ca_system_score_gemma":0.00008126527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003634754,"about_ca_topic_score_gemma":0.0002945793,"domain_scores_codex":[0.9972438,0.0001256214,0.0006158406,0.0002940508,0.001236694,0.0004840277],"domain_scores_gemma":[0.9989905,0.0001900089,0.0002225131,0.0001496198,0.0002725382,0.0001747629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002784408,0.0003009277,0.0539426,0.00005067314,0.0000299028,0.000003214384,0.001583963,0.1074077,0.817603,0.0003279619,0.000004447295,0.01846719],"study_design_scores_gemma":[0.0005141892,0.000580614,0.001060284,0.00006809631,0.00003060023,0.000008103811,0.000116215,0.96877,0.02443823,0.004234406,0.00005593241,0.0001233306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482234,0.00004807185,0.05107219,0.0001525944,0.0001672708,0.0002127558,0.000008995735,0.000006573524,0.000108141],"genre_scores_gemma":[0.9917759,0.00001528869,0.007928005,0.00001214922,0.0002262974,0.000003977544,0.000005274102,0.00000971626,0.00002342862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8613623,"threshold_uncertainty_score":0.5494685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09934856865065401,"score_gpt":0.3868281971451534,"score_spread":0.2874796284944994,"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."}}