Bibliographic record
Abstract
The long-term carbon storage function of pristine peatlands makes their disturbance or degradation particularly adverse for the global carbon cycle. In North America, the horticultural peat industry seeks to account for its drainage and extraction of Sphagnum moss-based peatlands by restoring them once commercial activity has ceased. Accordingly, researchers have developed a best-practices peatland restoration method anchored by the moss-layer transfer technique. While the vegetation, hydrology, and biodiversity dimensions of this method have been well studied, further research is needed to assess the carbon-exchange dimension. This thesis presents field research performed during the 2016 growing season at a post-extraction peatland near Seba Beach, Alberta, four years after the site was restored following current best practices. The research quantified CO2 and CH4 exchange at two scales: Eddy covariance (EC) towers measured ecosystem-scale fluxes, and static chambers measured plot-scale fluxes. Two EC towers were installed in separate areas of the peatland with visibly different vegetation cover. Disparate hydrological conditions have driven site-wide spatial heterogeneity in revegetation progress, particularly in Sphagnum moss cover, and the 2016 data from the two towers demonstrate that ecosystem-scale exchange of CO2 and CH4 was significantly spatially heterogeneous as a result. Plot-scale chamber measurements compared carbon fluxes over an infilled artificial drainage ditch with those over the adjacent peat field. The remnant ditch was a hotspot of CH4 efflux, and its denser vegetation boosted its photosynthetic CO2 uptake, particularly with the key graminoid species Eriophorum vaginatum. Both scales of research demonstrate the importance of a shallow water table to quick re-establishment of productive vegetation and favorable carbon exchange in restored post-extraction 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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".