Examining the peat-accumulating potential of fen vegetation in the context of fen restoration of harvested peatlands
Bibliographic record
Abstract
In order to focus efforts towards specific vegetation groups in fen restoration, knowledge of the peat-accumulating function of dominant fen species is critical. The decomposition rates of 3 species typical to undisturbed fens and 3 species that spontaneously colonize harvested fens were assessed. These species were incubated in both a restoration site (harvested fen) and an undisturbed fen to compare decomposition according to different environmental conditions. The average exponential decay coefficient (k) for all material types was slightly higher (circa 0.04·y−1 higher) in the harvested fen than those observed in the undisturbed fen. However, the litter type (leaves, roots/rhizomes, or bryophyte fragments) had the largest impact on the decomposition rates. The 2 tested bryophytes had lower k-values (between 0.14 and 0.11 for Polytrichum strictum and 0.06 for Sphagnum centrale) than the vascular plant litter (between 0.25 and 0.50). The annual primary production of the tested species was also measured to estimate the peat-accumulating capacity of each species. Scirpus cyperinus had an annual primary production that was 3 times higher (1500 g·m−2.y−1) than the other species (between 300 and 550 g·m−2.y−1). Estimates show that the harvested fen has a high peat-accumulating potential due to the high biomass production observed at this site.
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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) | 0.000 | 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".