{"id":"W4205346899","doi":"10.1038/s43247-021-00333-1","title":"The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management","year":2022,"lang":"en","type":"article","venue":"Communications Earth & Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":286,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; Guangdong Innovative and Entrepreneurial Research Team Program; National Aeronautics and Space Administration","keywords":"Vegetation (pathology); Environmental science; Carbon sequestration; Carbon sink; Greenhouse gas; Primary production; Carbon dioxide; Carbon cycle; Land use; Carbon fibers; Carbon dioxide in Earth's atmosphere; Sink (geography); Global warming; Soil carbon; Carbon flux; Land management; Atmospheric carbon cycle; Climate change; Ecosystem; Soil science; Ecology; Soil water; Geography; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0003403727,0.0001830107,0.0001491061,0.000006558927,0.0008530564,0.00002607172,0.001104952,0.00004041671,0.0002479461],"category_scores_gemma":[0.000004172071,0.000172599,0.00008616893,0.0001545488,0.0007030134,0.00005735441,0.001802486,0.0002004395,0.00001263318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005140828,"about_ca_system_score_gemma":0.00001119511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001334345,"about_ca_topic_score_gemma":0.000221532,"domain_scores_codex":[0.9980786,0.0003108608,0.0003849527,0.0002920788,0.0006389297,0.000294599],"domain_scores_gemma":[0.998293,0.00004644922,0.0002261524,0.001335519,0.000001150934,0.00009774636],"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":[0.0002439567,0.001046275,0.08159754,0.000007300914,0.0001752398,0.00001079322,0.0004534081,0.887316,0.003873897,0.0004451478,0.0003599583,0.02447048],"study_design_scores_gemma":[0.00435254,0.0007675932,0.4705908,0.00001527419,0.0004428849,0.00004009909,0.00267027,0.244211,0.0004095825,0.0006618287,0.2747693,0.001068736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912826,0.0005431894,0.002111279,0.00124394,0.0001069159,0.0006127072,0.00005591974,0.00002784056,0.004015605],"genre_scores_gemma":[0.9903228,0.001137216,0.007786857,0.0001190038,0.00001033101,0.00009668218,0.0001503858,0.00001848546,0.000358221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.643105,"threshold_uncertainty_score":0.7038385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007321501407798335,"score_gpt":0.1993725386879298,"score_spread":0.1920510372801315,"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."}}