{"id":"W2766958676","doi":"10.5194/bg-15-2433-2018","title":"Water-stress-induced breakdown of carbon–water relations: indicators from diurnal FLUXNET patterns","year":2018,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Oak Ridge National Laboratory; Canadian Foundation for Climate and Atmospheric Sciences; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; University of Virginia; Natural Resources Canada; Microsoft Research; Max-Planck-Gesellschaft; Université Laval; Università degli Studi della Tuscia; U.S. Department of Energy; National Science Foundation","keywords":"Eddy covariance; FluxNet; Environmental science; Evapotranspiration; Atmospheric sciences; Evergreen; Stomatal conductance; Transpiration; Ecosystem; Ecology; Photosynthesis; Biology; Botany","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":[0.0005190664,0.0002525197,0.0002225489,0.001283899,0.0002235713,0.0004789839,0.0002235184,0.0002583513,0.001074313],"category_scores_gemma":[0.001035085,0.00009344289,0.0001567209,0.001473365,0.0001797558,0.0004337316,0.0003407016,0.0001582656,0.0002009413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138819,"about_ca_system_score_gemma":0.0001023188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002392959,"about_ca_topic_score_gemma":0.003700888,"domain_scores_codex":[0.9998395,0.00003411672,0.00001547181,0.00005078773,0.00003599926,0.00002403407],"domain_scores_gemma":[0.9992976,0.0001651143,0.0002544607,0.00006567455,0.0001422261,0.00007488875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003619487,0.00006991313,0.9365705,0.0001385616,0.0001140403,0.00008904072,0.0003750885,0.002636211,0.03873946,0.0003098217,0.0009164002,0.01967896],"study_design_scores_gemma":[0.000002148081,0.00002081613,0.995486,0.000003785931,0.000005447375,0.00005662897,0.00006486119,0.002470655,0.001210914,0.00009907851,0.0005737242,0.000005801544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945581,0.0001597697,0.001908668,0.0000243051,0.000008836872,0.00001142686,0.002162408,0.00009796608,0.001068454],"genre_scores_gemma":[0.9964455,0.0000471725,0.0009156239,0.00001274469,0.00001117651,0.00001791995,0.002329828,0.0000217872,0.0001982997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002392959,"threshold_uncertainty_score":0.00475806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008071816797152725,"score_gpt":0.2042112797079788,"score_spread":0.1961394629108261,"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."}}