{"id":"W2222009108","doi":"10.1016/j.agrformet.2015.12.059","title":"Ten-year variability in ecosystem water use efficiency in an oak-dominated temperate forest under a warming climate","year":2016,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":86,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Southern Research Station; College of Engineering, Michigan State University; McMaster University; Michigan State University","keywords":"Evapotranspiration; Eddy covariance; Water-use efficiency; Environmental science; Growing season; Ecosystem; Vapour Pressure Deficit; Atmospheric sciences; Temperate climate; Leaf area index; Biomass (ecology); Precipitation; Agronomy; Ecology; Transpiration; Biology; Geography; Botany; Irrigation; Photosynthesis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005765467,0.0001824503,0.0002373546,0.000057118,0.00008716889,0.000038018,0.0001440569,0.0001623347,0.00006452308],"category_scores_gemma":[0.00002180134,0.00007917352,0.00003356464,0.0001703663,0.0001121788,0.0005251604,0.0001689038,0.0001195215,0.00006829879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001235576,"about_ca_system_score_gemma":0.000003169932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000441579,"about_ca_topic_score_gemma":0.01489806,"domain_scores_codex":[0.9983631,0.0002570476,0.0003338406,0.0004312329,0.0001014764,0.0005132959],"domain_scores_gemma":[0.9995818,0.0001138086,0.0000491864,0.0001506176,0.00001118297,0.00009344352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004976808,0.00007572824,0.9551057,0.000005729635,0.000004192843,0.00001313634,0.0001972636,0.004316259,0.03917763,0.0007198784,0.000003174579,0.0003316011],"study_design_scores_gemma":[0.0007937481,0.0001247268,0.9894562,0.00001738581,0.00001065884,0.00005704911,0.0000520538,0.007939217,0.0003567981,0.0009215699,0.00006537252,0.0002051968],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990443,0.000003521642,0.00006412699,0.0003078805,0.0000921676,0.000270818,0.00003254728,0.00002718459,0.0001574381],"genre_scores_gemma":[0.9996061,0.0000279198,0.0001057828,0.00004773696,0.00001232981,0.00003230655,0.00005323561,0.000006879994,0.0001077504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03882083,"threshold_uncertainty_score":0.8313469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006825156846452974,"score_gpt":0.1895305841192331,"score_spread":0.1827054272727801,"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."}}