The contribution of rewetting to vegetation restoration of degraded peat meadows
Bibliographic record
Abstract
Abstract Question: What is the contribution of a rise in groundwater level to vegetation restoration of degraded peat meadows compared to abandonment only? Location: Abandoned peat meadows in the central part of The Netherlands. Methods: Comparison of species composition and species abundance of vegetation and seed banks of reference and rewetted peat meadows, using plant trait and seed bank analysis. Results: Vegetation of rewetted meadows shared on average only 27% of their species with the reference meadow, while this was 50% on average for species in the seed bank. Rewetted meadows had a lower total number of species and a lower number of wet grassland and fen species present in the vegetation, but had higher species richness per m2, although evenness was not affected. Rewetting increased the dominance of species of fertile and near neutral habitats, but did not result in an increase of species of wet or waterlogged habitats. Re‐wetted meadows were dominated by species relying mainly on vegetative reproduction and species with a low average seed longevity compared to the reference meadow. Conclusion: Rewetting was not effective as a restoration measure to increase plant species diversity or the number of wet grassland and fen species in the vegetation. If no additional restoration management is applied, the seed bank will be depleted of seeds of species of wet grassland or fen habitats, further reducing the chances of successful vegetation restoration.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".