Rewetting increases vegetation cover and net growing season carbon uptake under fen conditions after peat-extraction in Manitoba, Canada
Bibliographic record
Abstract
Abstract The moss layer transfer technique has been developed to restore the carbon sequestration function and typical vegetation ofSphagnum-dominated peatlands after peat extraction in North America. However, the technique does not lead to successful bryophyte establishment when applied to peatlands with a richer residual fen peat. Therefore, we evaluated an alternative method of active rewetting and passive vegetation establishment using vegetation surveys and carbon dioxide and methane (CH4) flux measurements at a post-extracted fen in southern Manitoba, Canada. After one growing season post-rewetting, wetland vegetation established and the site was a net carbon sink over the growing season. However, high abundance ofCarex lasiocarpa10 years post-treatment led to higher CH4emissions than the reference ecosystem. Successful establishment of wetland vegetation is attributed to the area being surrounded by undisturbed fens that can provide a local source of plant propagules. Bryophyte expansion was less successful than vascular plants, likely due to episodic flooding and shading from the sedge communities. Therefore, careful management of water levels to just below the peat surface is needed if reference vegetation community recovery is the goal of restoration. Water level management will also play a key role in controlling CH4emissions to maximize carbon sequestration potential.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".