Vertical and seasonal changes in soil carbon pools to vegetation degradation in a wet meadow on the Qinghai-Tibet Plateau
Bibliographic record
Abstract
) released into the atmosphere by increasing the soil organic carbon (SOC) stored in wetland systems. Although wet meadows serve as the most important and stable C sinks, there has been very few investigations on the seasonal distributions of SOC fractions in high-altitude wet meadows. Here, we studied the effects of four vegetation degradation levels, non-degraded (ND), lightly degraded (LD), moderately degraded (MD), and heavily degraded (HD), on the measured vertical and seasonal changes of SOC and its different fractions. Among these vegetation degradation levels, 0-10 and 10-20 cm soil depths in ND plots had significantly higher SOC contents than the other degradation levels had throughout the year. This is attributed to the relatively greater inputs of aboveground plant litter and richer fine-root biomass in ND plots. Particulate organic carbon (POC) and light fraction organic carbon (LFOC) showed similar vertical and seasonal variations in autumn, reaching a minimum. Moreover, microbial biomass (MBC) and easily oxidizable organic carbon (EOC) contents were highest in summer and the smallest in winter, while dissolved organic carbon (DOC) content was highest in spring and lowest in summer, and were mainly concentrated in the 0-20 cm layer. Pearson correlation analysis indicated that soil properties and aboveground biomass were significantly related to different SOC fractions. The results indicate that vegetation degradation reduces the accumulation of total SOC and its different fractions, which may reduce carbon sink capacity and soil quality of alpine wet meadows, and increase atmospheric environmental pressure. In addition, vegetation biomass and soil characteristics play a key role in the formation and transformation of soil carbon. These results strengthen our understanding of soil C dynamics, specifically related to the different C fractions as affected by vegetation degradation levels and soil depth, in wet meadow systems.
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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".