Editorial: Vegetation-based degradation and restoration on the alpine grasslands of the Tibetan plateau
Bibliographic record
Abstract
Vegetation-based degradation and restoration on the alpine grasslands of the Tibetan plateauThe Tibetan Plateau is the world's highest and largest plateau, often referred to as 'the roof of the world' due to its high elevation and extensive mountain systems, which host hotspots of endemic biodiversity.It is also known as the "third pole" because of its cold environment, and the "Asian water tower" owing to its rich river systems.This unique physical and geographical environment supports a variety of alpine ecosystems, predominantly classified as alpine grasslands (Figure 1).These grasslands cover approximately 50-70% of the Tibetan plateau's total land area (Wu et al., 2021;Zhang et al., 2022).Such a range in estimates arises from varying definitions of grassland according to different authorities and scientists, particularly concerning the inclusion of cropland, desert, or shrublands.Most of these rangelands feature a fragile, tundra-like environment that is difficult to restore once degraded (Niu et al., 2019) due to the slow soil formation processes, low nutrient availability, limited seed recruitment, and the short growing season (Li et al., 2023; Niu et al., 2023a).The main natural grassland types on the Tibetan plateau include alpine deserts, alpine steppes, alpine meadows, alpine swamps, and the transitional types between them varying along the precipitation gradient.The alpine grasslands continue to experience severe degradation due to multiple factors (Liu et al., 2018; Niu et al., 2023b), primarily anthropogenic disturbances (e.g.overgrazing, road construction), as well as natural environmental changes (e.g.climate change, rodent outbreak).As a result, its degradation presents a major global environmental threat given the importance of Tibetan grasslands to biodiversity, ecosystem complexity, and provision of ecosystem services.This Research Topic tried to explore the current state of vegetation-based alpine grassland degradation, the causes of this degradation and possible solutions to restore and re-establish the local plant communities.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".