Experimental restoration of a fen plant community after peat mining
Bibliographic record
Abstract
Abstract Question: Which restoration measures (introduction of donor diaspore material, application of straw mulch, alteration of residual peat depths) contribute to the establishment of a fen plant community on minerotrophic surfaces after peat mining? Location: Rivière‐du‐Loup peatland, southern Québec, Canada at 100 m a.s.1. Methods: The effectiveness of introducing fen plants with the application of donor diaspore material was tested. The donor diaspore material, containing seeds, rhizomes, moss fragments, and other plant propagules, was collected from two different types of natural fens. We tested whether the application of straw mulch would increase fen species cover and biodiversity compared to control plots without straw mulch. Terrace levels of different peat depths (15 cm, 40 cm, and 56 cm) were created to test the effects of different environmental site conditions on the success of re‐vegetation. Results: Applying donor seed bank from natural fens was found to significantly increase fen plant cover and richness after the two growing seasons. Straw mulch proved to significantly increase fen plant richness. The intermediate terrace level (40 cm) had the highest fen plant establishment. Compared to reference sites, the low terrace level (15 cm) was richer in base cations, whereas the high terrace level (56 cm) was much drier. Conclusions: The application of donor diaspore material was demonstrated as an effective technique for establishing vascular fen plants. Further re wetting measures are considered necessary at the restoration site to create a fen ecosystem rather than simply restoring some fen species.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".