Phenolic supplements: testing an approach to limit Sphagnum decomposition in a Sphagnum farming system
Bibliographic record
Abstract
Phenolic compounds have been proposed to influence decomposition by inhibiting extracellular enzyme activities, as described in the enzymic latch mechanism (ELM). This study examined the effects of phenolic treatments on Sphagnum decomposition, productivity, and biomass accumulation within a Sphagnum farming system. A split-plot experiment with three phenolic treatments was implemented in two cultivation basins established with mosses dominated by the Acutifolia or Sphagnum subgenus. Phenolic treatments were wood pellets (wood), old roots from peat harrowing (root), and no addition (control). Phenolic additions did not result in a measurable reduction in decomposition rates nor was Sphagnum productivity or biomass affected by the experimental treatments. Both subgenera functioned as approximately similar small carbon dioxide (CO 2 ) sinks, with values such as −2 ± 1 g CO 2 m −2 d −1 ( Acutifolia ) and −0.2 ± 0.8 g CO 2 m −2 d −1 ( Sphagnum ). Phenolic additions in both subgenera resulted in higher CO 2 values as net ecosystem exchange compared to the control, which could be linked to emissions resulting from wood and root decomposition. In both subgenera, phenolic additions neither increased peat phenolic concentrations nor inhibited enzyme activities compared to the control. The current study did not validate the potential of phenolics in limiting decomposition as theorized in the ELM. The short duration of the experiment may have restricted the effect of phenolic products applied at the surface from reaching the ∼10 cm depth where peat was sampled. This could explain the absence of an inhibitory effect of phenolic products on enzyme activities. Therefore, it is recommended to conduct various sample analyses at different depths to better understand phenolic–enzyme interactions in a Sphagnum farming system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".