{"id":"W2916043000","doi":"10.1111/gcb.14602","title":"Anticipating global terrestrial ecosystem state change using FLUXNET","year":2019,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Directorate for Biological Sciences; University of Reading; National Science Foundation; Royal Society; University of Cambridge; University of Virginia; Cambridge Philosophical Society; Université Laval; Oak Ridge National Laboratory; Biological and Environmental Research; Canadian Foundation for Climate and Atmospheric Sciences; University of California; Microsoft Research; Environment Canada; U.S. Geological Survey; Microsoft; Curtin University of Technology; U.S. Department of Energy","keywords":"FluxNet; Ecosystem; Environmental science; Terrestrial ecosystem; Global change; Eddy covariance; Climate change; Primary production; Atmospheric sciences; Macroecology; Ecology; Climatology; Biodiversity; Physics; Geology; Biology","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":[0.0009373731,0.0007455936,0.0003893681,0.002086326,0.0003307318,0.0007771053,0.000722711,0.0008574487,0.001942288],"category_scores_gemma":[0.002594628,0.0002705661,0.0006218976,0.001608665,0.0002556479,0.001433224,0.0005406268,0.0005196867,0.0004291657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009463572,"about_ca_system_score_gemma":0.0004652627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02338058,"about_ca_topic_score_gemma":0.03888056,"domain_scores_codex":[0.9997965,0.00004917897,0.00001605616,0.00007357328,0.00003487115,0.00002979026],"domain_scores_gemma":[0.9994769,0.0002056865,0.00009594674,0.0000651616,0.0001217688,0.00003458582],"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":[0.0003335723,0.0002930878,0.2294136,0.000580359,0.0004185392,0.0005216886,0.0003527697,0.6497962,0.003125687,0.0114414,0.04247152,0.06125161],"study_design_scores_gemma":[0.00003892694,0.00004479989,0.03817295,0.00005662669,0.00003579192,0.00005719822,0.0001626248,0.9364674,0.001218077,0.01002969,0.01368675,0.00002910529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7738971,0.001018605,0.05301752,0.001408893,0.000258166,0.0001402982,0.1546914,0.006674953,0.008893104],"genre_scores_gemma":[0.7720616,0.0004057934,0.04274124,0.0002421831,0.00007245137,0.000241418,0.1828665,0.0001935096,0.001175357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02338058,"threshold_uncertainty_score":0.046489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0480344651051654,"score_gpt":0.2985920225932364,"score_spread":0.250557557488071,"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."}}