Water table drawdown increases plant biodiversity and soil polyphenol in the Zoige Plateau
Bibliographic record
Abstract
Water table drawdown accelerates peatland degradation and carbon loss from peatland, but phenolic compounds—the concentration and composition of which are determined by vegetation community composition—can slow down carbon loss. However, the response of phenolic compounds to water table drawdown is not clear. We aimed to clarify how water table drawdown influence soil phenolic compounds composition by detecting them at peatlands with different water table. Our results showed that water table drawdown changed plant biodiversity and altered the structure of phenolic compounds. Plant biodiversity, richness, evenness, areal coverage, and aboveground biomass all significantly increased with the water table drawdown. Phenolic compounds transferred from monophenol to polyphenol with the water table drawdown. The concentration of water-soluble phenols also increased with the water table drawdown due to the hydrophilic nature of polyphenol compounds. In addition, the concentration of water-soluble phenols was positively correlated with total vegetation coverage and richness. We concluded that water table drawdown accelerates change in the vegetation community, alters the structure of phenolic compounds and increased the concentration of water-soluble phenols, which could play an important role in carbon output from peatlands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".