Restoring metal contaminated peatlands in Sudbury, Ontario
Bibliographic record
Abstract
Industrial contamination has profoundly impacted peatland ecosystems, degrading their biodiversity and essential functions such as carbon sequestration. The Sudbury region in Ontario, Canada is one of the world's largest metal mining centres and historically the largest global point source of sulfur and metal pollution and serves as a critical case study for understanding and addressing these impacts. Peatlands closest to pollution sources have suffered extensive degradation, with keystone vegetation, including Sphagnum mosses, locally extinct and peat layers showing significant carbon losses. Developing innovative restoration techniques is crucial before undertaking regional-scale restoration of metal-impacted peatlands, ensuring chemical stressors are overcome effectively while minimizing sequestered metal release. In collaboration with regional stakeholders and academic institutions, our interdisciplinary team is pioneering innovative restoration techniques to reinstate peatland functionality in this toxic metal and metalloid-polluted landscape. Building on established practices, such as the moss-layer transfer technique, our modified approaches incorporate surface tilling, mulching, fertilization, and the reintroduction of donor peatland material. These interventions aim to overcome chemical stressors like persistent high concentrations of water-extractable metals (e.g., copper and nickel), which inhibit Sphagnum recovery. A restoration field trial began in fall 2023 with surface mulching, and in spring 2024 we applied restoration treatments of mulch, fertilizer, and planting. Here, we present results from the first growing season for peat chemistry, hydrology, greenhouse gas fluxes, and vegetation.
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".