The global extent of the grassland biome and implications for the terrestrial carbon sink
Bibliographic record
Abstract
Acknowledgements: We thank each of the researchers who have contributed data and ideas to this paper. This study was largely funded by the Canada First Research Excellence Fund—University of Guelph (‘Food from Thought’), with support from the Natural Sciences and Engineering Research Council of Canada (A.S.M.). M.B.S. acknowledges funding from the Swedish Research Council (2021-05767), FORMAS (2020-01073) and the European Union’s Horizon Program project ILLUQ (no. 101133587). Funding was also provided to E.W.S. and E.T.B. by the National Science Foundation Research Coordination Network (NSF-DEB-1042132) and the Long-Term Ecological Research (NSF-DEB-1234162 to Cedar Creek LTER) programmes, and the Institute on the Environment (DG-0001-13). Y.M.B. acknowledges financial support from Research Ireland, Northern Ireland’s Department of Agriculture, Environment and Rural Affairs (DAERA), UK Research and Innovation (UKRI) via the International Science Partnerships Fund (ISPF) under grant number [22/CC/11103] at the Co-Centre for Climate + Biodiversity + Water. N.E. was supported by the German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig iDiv funded by the German Research Foundation (DFG– FZT 118, 202548816), and funding by the DFG (Ei 862/29-1). S.C.P. acknowledges funding from NSF OCE-1832178.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".