{"id":"W3190359607","doi":"10.3389/fenvs.2021.689301","title":"On the Treatment of Soil Water Stress in GCM Simulations of Vegetation Physiology","year":2021,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Horizon 2020 Framework Programme; Universidad de Sevilla; Université Laval; Microsoft Research; Oak Ridge National Laboratory; Sight Research UK; Fundación Ramón Areces; Natural Environment Research Council; Met Office; U.S. Department of Energy; National Science Foundation","keywords":"Vegetation (pathology); Permanent wilting point; Water content; Environmental science; Temperate climate; Wilting; Soil water; Hydrology (agriculture); Atmospheric sciences; Soil science; Ecology; Biology; Geology; Botany; Field capacity","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":[0.001652474,0.00140691,0.001241809,0.0006115644,0.00165221,0.002002597,0.002846057,0.003424651,0.006268112],"category_scores_gemma":[0.008502636,0.0006425612,0.001343274,0.00169086,0.001300368,0.002138285,0.001684963,0.003860744,0.001345185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392185,"about_ca_system_score_gemma":0.001083442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03324477,"about_ca_topic_score_gemma":0.02482476,"domain_scores_codex":[0.9993218,0.0003746197,0.00003752903,0.00007582258,0.0001330365,0.00005717142],"domain_scores_gemma":[0.9973812,0.00170376,0.0001518389,0.0003078822,0.0003229548,0.0001324138],"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":[0.0002109341,0.00009567357,0.002678813,0.0002483904,0.0001207429,0.0001640339,0.0002497426,0.9195471,0.003184091,0.03974894,0.0113939,0.02235764],"study_design_scores_gemma":[0.0000652286,0.00002396175,0.0006297317,0.00004936054,0.00001951163,0.00001284725,0.00002971542,0.9829839,0.0009566405,0.007255395,0.007950632,0.00002311352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1387508,0.005794149,0.7044309,0.009706364,0.002906218,0.001003602,0.00909589,0.01044472,0.1178674],"genre_scores_gemma":[0.8192613,0.003471121,0.1449814,0.003975437,0.00108627,0.00149153,0.00278633,0.00374018,0.0192064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03324477,"threshold_uncertainty_score":0.06610256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00466592030964063,"score_gpt":0.1864474170284858,"score_spread":0.1817814967188451,"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."}}