{"id":"W2143941656","doi":"10.1002/2013jg002553","title":"Comprehensive ecosystem model‐data synthesis using multiple data sets at two temperate forest free‐air CO<sub>2</sub> enrichment experiments: Model performance at ambient CO<sub>2</sub> concentration","year":2014,"lang":"en","type":"article","venue":"Journal of Geophysical Research Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Oak Ridge National Laboratory; Biological and Environmental Research; Office of Science; National Center for Ecological Analysis and Synthesis; UT-Battelle; Battelle; National Centre for Earth Observation; U.S. Department of Energy","keywords":"Evergreen; Transpiration; Leaf area index; Temperate rainforest; Environmental science; Temperate forest; Canopy; Deciduous; Range (aeronautics); Temperate deciduous forest; Forest ecology; Primary production; Temperate climate; Basal area; Atmospheric sciences; Ecosystem; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002476775,0.0003489912,0.00050227,0.0001761133,0.001288099,0.0002497312,0.002828591,0.0001151311,0.00001140816],"category_scores_gemma":[0.0002660906,0.0002697939,0.0001135784,0.0005885737,0.0008862044,0.002211641,0.002837774,0.000462512,0.0001167488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056086,"about_ca_system_score_gemma":0.0002016824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006294321,"about_ca_topic_score_gemma":0.0007482021,"domain_scores_codex":[0.9936447,0.0004935443,0.0008459578,0.0009666117,0.003033278,0.001015893],"domain_scores_gemma":[0.996869,0.0004641695,0.0005274244,0.001398906,0.0001912911,0.0005491912],"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":[0.000147412,0.0002693456,0.02552174,0.00002547734,0.00002771667,0.00001050485,0.0001795081,0.1521024,0.8198821,0.00002143138,0.0005682372,0.001244121],"study_design_scores_gemma":[0.0005341591,0.0001940569,0.006526661,0.00008983252,0.00002584099,0.00003503621,0.00006947099,0.7882477,0.2037558,0.0001232848,0.0001443058,0.0002539057],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954889,0.00004704257,0.002490188,0.0001676089,0.0001549364,0.0004542353,0.001073349,0.00001950157,0.0001042675],"genre_scores_gemma":[0.998063,0.0003376329,0.001164441,0.00007050051,0.000102032,0.00001658903,0.0001990239,0.00002340164,0.00002339118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6361452,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0769242972206749,"score_gpt":0.3234382322713698,"score_spread":0.2465139350506949,"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."}}