Environmental influences on plant diversity in restored and natural peatland pools
Bibliographic record
Abstract
Peatland pools are crucial habitats for biodiversity, but peat extraction removes vegetation and drains sites, often eliminating pools. This study compared environmental conditions and vegetation in 59 pools from 14 restored Sphagnum peatlands (≥7 years post-restoration) with 26 pools from nine reference peatlands in Eastern Canada. We examined three restored pool types: (1) created pools, (2) spontaneous pools, and (3) open water in ditches. Pool type explained 19% of the variation in species composition, while regionality, morphology, and water chemistry accounted for 32%. Larger restored pools resembled reference pools morphologically and had higher dissolved organic carbon and nitrogen. Spontaneous pools exhibited elevated nitrogen (2.1 mg/L vs. 0.57 mg/L) and phosphorus (0.08 mg/L vs. 0.02 mg/L), and a higher pH that may favor non-target species, e.g., Typha latifolia. Reference pools had a uniform composition, comprising 20 associated species, whereas restored pools exhibited greater variability, reduced richness, and higher beta diversity. Post-restoration surveys emphasized the importance of deeper pools for water retention and chemical stability. Complementary measures, such as reintroducing typical pool species, are essential to ensure their establishment and enhance plant biodiversity in restored peatlands.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".