Impacts of livestock grazing on vegetation characteristics and soil chemical properties of alpine meadows in the eastern Qinghai-Tibetan Plateau
Bibliographic record
Abstract
Livestock grazing is one of the significant causes of land degradation. However, the effect of contrasting grazing intensities on soil properties and vegetation in the southeastern Qinghai-Tibetan Plateau (QTP) is poorly understood. We studied the impact of light grazing (LG), moderate grazing (MG), heavy grazing (HG) and no grazing (NG) on vegetation characteristics and the chemical properties of soil samples taken at 0–10 cm, 10–20 cm and 20–30 cm layers from the designated grazing treatments. A total of 42 species representing 32 genera and 16 families were identified. Our result shows that HG significantly reduced total aboveground biomass, vegetation cover, canopy average height, but increased unpalatable aboveground biomass. Soil organic matter declined with increasing grazing intensity and respectively decreased to 64.51%, 65.38% and 82.40% for LG, MG and HG compared to the NG treatment and soil carbon storage exhibited a similar pattern. Soil total nitrogen and phosphorus contents decreased with increasing soil depth, while soil total potassium was not affected by grazing across soil depths. We conclude that 1 yak would have a more severe impact than 3 sheep units on the vegetation community and soil characteristics of alpine meadows in the southeastern QTP.
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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 | 0.000 | 0.000 |
| 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".