Bibliographic record
Abstract
Peatland restoration aims to re-establish carbon (C) sequestration and peat accumulation to the ecosystem following a disturbance. While bog restoration has been relatively well-studied, limited data is available on C exchange following fen restoration in Canada. My research was conducted at South Julius, Manitoba, a post-extraction peatland restored in 2016. Differing restoration treatments created contrasting hydrological conditions and thus plant communities across the site. The “wet” site included reprofiling, creation of berms, and spreading of donor material. Rainy conditions the following spring caused flooding that spread to active extraction areas. In response, a berm was installed which resulted in deep inundation. This site is now a marsh-like plant community currently dominated by Typha spp. in standing water. The “dry” site did not include reprofiling but was rewetted during restoration activities and developed a diverse plant community with sedges and shrubs. The restored areas are surrounded by undisturbed fens. Eddy covariance (EC) systems were installed in the dry, wet, and undisturbed sites to measure ecosystem-scale C fluxes in addition to hydrological and meteorological conditions, providing insights into differences in C exchange between the sites. Preliminary findings indicate that the undisturbed fen exhibits the highest net C uptake, while the wet site has elevated methane (CH4) emissions, likely linked to plant-mediated transport through the Typha spp. These findings highlight how hydrological outcomes and plant community development shape ecosystem greenhouse gas (GHG) function following restoration. My research will assess restoration outcomes and results will be important for GHG emission reporting for C offset credits and Canada’s national GHG inventory. This research was conducted under the supervision of Dr. Ian Strachan in the Atmospheric and Environmental Research (AER) Lab, Department of Geography and Planning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".