{"id":"W2889206922","doi":"10.5194/hess-22-4513-2018","title":"Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements","year":2018,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Northwest Research Station; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; National Aeronautics and Space Administration; Natural Resources Canada; Jet Propulsion Laboratory; Institut National Du Cancer; Biological and Environmental Research; Canadian Foundation for Climate and Atmospheric Sciences; U.S. Department of Energy; California Institute of Technology; European Space Agency; University of Montana","keywords":"Extrapolation; FluxNet; Tower; Weighting; Environmental science; Mathematics; Variance (accounting); Statistics; Meteorology; Geography; Eddy covariance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002701348,0.0008310407,0.000362481,0.0008926972,0.0002238866,0.001124883,0.0005895748,0.0004983874,0.0008136372],"category_scores_gemma":[0.003466638,0.0003970378,0.0007767543,0.001498067,0.0001811374,0.00192013,0.0008940336,0.0004268834,0.0002948508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004874297,"about_ca_system_score_gemma":0.0003927035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009592621,"about_ca_topic_score_gemma":0.01343979,"domain_scores_codex":[0.9994735,0.0001869199,0.00003043896,0.0001650102,0.0001041111,0.00004005596],"domain_scores_gemma":[0.9992549,0.0002099384,0.00009223958,0.0001307186,0.0002590268,0.00005328738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001014804,0.0003833463,0.2544443,0.0003494933,0.001142388,0.0004476007,0.0008572654,0.4946125,0.0399755,0.002109539,0.002789997,0.2018732],"study_design_scores_gemma":[0.00006879216,0.0002106898,0.1599553,0.00005305549,0.000212418,0.00005866764,0.0002980735,0.8220894,0.01219886,0.0008415601,0.003955651,0.00005749242],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645144,0.0001762921,0.03091534,0.00009687537,0.00004450485,0.00006763785,0.001445243,0.0007261269,0.002013604],"genre_scores_gemma":[0.9625476,0.00005819434,0.03389306,0.0000218163,0.00001512056,0.00003191823,0.00291556,0.0001694912,0.0003471935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009592621,"threshold_uncertainty_score":0.01907355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04970504749709468,"score_gpt":0.2254466251597262,"score_spread":0.1757415776626315,"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."}}