{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002770244,0.0002310476,0.0003213207,0.00001731128,0.000106107,0.00003317525,0.0003877861,0.0001729773,0.0003369563],"category_scores_gemma":[0.00001369598,0.0001935651,0.00008069789,0.0003212946,0.00007503024,0.0001873616,0.0004423753,0.00007621788,0.001029224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006052434,"about_ca_system_score_gemma":0.00001366532,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007373346,"about_ca_topic_score_gemma":0.003568044,"domain_scores_codex":[0.9981114,0.0001531675,0.0003156786,0.0005559011,0.0001730194,0.0006908097],"domain_scores_gemma":[0.9993162,0.00001909497,0.0002003261,0.0003088392,0.000007749485,0.000147848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002706874,0.00002709582,0.9926257,0.0000205311,0.00001142299,0.00001222458,0.0001018379,0.00002835856,0.0005788882,0.0001532823,0.00002639014,0.006387255],"study_design_scores_gemma":[0.002701194,0.001084384,0.7032183,0.0002823672,0.00006232728,0.0002983429,0.0003369563,0.2750039,0.00006458093,0.001558265,0.01385804,0.001531375],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927888,0.0001632116,0.0001587502,0.000073554,0.002238605,0.000753426,0.0005033115,0.00006015492,0.003260173],"genre_scores_gemma":[0.9989972,0.00002067602,0.0002904989,0.0002286519,0.0003668951,0.00003308326,0.00003200476,0.00000908418,0.00002191309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2894074,"threshold_uncertainty_score":0.9997486,"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."}}