Assessing the Spatiotemporal Dynamic of Global Grassland Water Use Efficiency in Response to Climate Change from 2000 to 2013
Bibliographic record
Abstract
Abstract Water use efficiency (WUE), which is a ratio of net primary production (NPP) to evapotranspiration (ET), is an important index representing the relationship between carbon and water cycles. This study evaluates the spatiotemporal dynamics of global grassland WUE from 2000 to 2013 to reveal the different responses of each grassland type to climate variations. Their correlations with climate variables are also investigated to reflect their dependence on climate. The average annual WUE of different grassland types follows an order of: closed shrublands > woody savannas > savannas > open shrublands > non‐woody grasslands. Although the NPP of all grassland types has increased from 2000 to 2013, 37.89 % of grassland ecosystems globally experienced a decreased WUE, in which 3.34 % has extremely significantly decreased. The WUE of open shrublands, woody savannas and non‐woody grasslands shows an overall descending trend because of the exceeding increasing rate of ET. By contrast, the decreased ET contributes to the overall ascending trend of the WUE of closed shrublands and savannas over this period. Moreover, the WUE of each grassland type reacts differently to climate variations in the northern and southern hemispheres. The grassland WUE dynamic is more controlled by precipitation than temperature at a global scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".