Sphagnum mosses cultivated in outdoor nurseries yield efficient plant material for peatland restoration
Bibliographic record
Abstract
Sphagnum mosses are often reintroduced for peatland restoration or needed for the initiation of cultivation basins for Sphagnum farming. Finding Sphagnum dominated peatland where plant collection is permitted can be challenging and hampers peatland restoration in some regions. Theoretically, starting from small initial collections in natural areas, Sphagnum could be multiplied at Sphagnum cultivation sites and then be used as donor plant material for restoration. However, it is uncertain whether cultivated Sphagnum possesses the same regeneration capacity as moss fragments originating from natural peatlands. In this study we compared the establishment of Sphagnum mosses and peatland plant diversity on experimental plots that were revegetated with cultivated Sphagnum and Sphagnum originating from natural peatland. We found that reintroducing cultivated Sphagnum carpets of thickness > 5 cm and carpets collected from natural peatlands resulted in the same Sphagnum establishment. The cover of vascular plants and the diversity of peatland plants were similar in plots restored using cultivated Sphagnum and plots that were revegetated with plant material collected from natural peatland. If the cultivated plant material is to be used for restoration purposes, the donor site for initiating the Sphagnum cultivation site should contain a high peatland plant diversity.
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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 | 0.000 | 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".