Effect of repeated mowing to reduce graminoid plant cover on the moss carpet at a Sphagnum farm in North America
Bibliographic record
Abstract
Sphagnum farming is defined as the sustainable production of non-decomposed Sphagnum biomass on a cyclical and renewable basis. In this article, the influence and necessity of mowing graminoid plants to optimise Sphagnum growth in Sphagnum farming basins are examined. Repeated mowing was applied to reduce graminoid plant cover at two different stages of the production cycle (one-year-old and seven-year-old Sphagnum moss carpet at the beginning of the experiment) at an experimental Sphagnum farm in eastern Canada. Sphagnum growth (cover, biomass, moss layer thickness) was measured after three years of mowing. In addition, a greenhouse experiment was carried out to determine whether there is a threshold for graminoid plant cover beyond which mowing becomes necessary. Sphagnum cover and biomass were not affected by repeated mowing, even if mowing reduced the cover of graminoid plants. Thus, it appears that mowing is unnecessary if the dominant vascular species is a graminoid plant such as Eriophorum angustifolium, which accumulates minimal amounts of litter. Furthermore, high cover of Eriophorum angustifolium (up to 85 %) did not affect Sphagnum cover in a density-controlled greenhouse experiment. When the specific goal is Sphagnum fibre production, decisions about control of graminoid plants should be made after considering the cover, life form and litter accumulation potentials of the dominant graminoid species involved.
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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".