Areas with High Fractional Vegetation Cover in the Mu Us Desert (China) Are More Susceptible to Drought
Bibliographic record
Abstract
Largescale vegetation reconstruction projects in the western and northern parts of China, along with climate change and increased humidity, have significantly boosted fractional vegetation cover (FVC) in the Mu Us Desert. However, this increase may impact the area’s vulnerability to drought stress. Here, we assessed the area’s susceptibility to hydrometeorological drought by analyzing the maximum correlation coefficients (MCC) derived from the spatiotemporal relationships between FVC and estimates of standardized precipitation evapotranspiration index (SPEI) for the area. The results of the study were as follows: (1) FVC exhibited an increasing trend throughout the growing seasons from 2003 to 2022. Although the region experienced an overall wetting trend, drought events still occurred in some years. MCC-values were predominantly positive across all timescales, suggesting that vegetation generally responded favorably to drought conditions. (2) The order of response of land covertype to drought, from greatest to lowest, was grassland, cultivated land, forestland, and sand land. Cultivated land and grassland exhibited heightened sensitivity to short-term drought; forestland and sand land showed greater sensitivity to long-term drought. (3) With a high FVC, the response of grassland and sand land to drought was significantly enhanced, whereas the response of cultivated land and forestland was less noticeable. (4) Low FVC grassland and sand land have not yet reached the VCCSW threshold and can support moderate vegetation restoration. In contrast, forestland and cultivated land exhibit drought sensitivity regardless of FVC levels, indicating that increasing vegetation should be approached with caution. This research offers a method to evaluate the impact of drought stress on ecosystem stability, with findings applicable to planning and managing vegetation cover in arid and semiarid regions globally.
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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 | 0.000 |
| 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".