An overview of peatland restoration in North America: where are we after 25 years?
Bibliographic record
Abstract
Abstract Peatland restoration in North America ( NA ) was initiated approximately 25 years ago on peat‐extracted bogs. Recent advances in peatland restoration in NA have expanded the original concepts and methodology. Restoration efforts in NA now include restoring peatlands from many diverse types of disturbances (e.g. roads, agriculture, grazing, erosion, forestry, and petrol industry infrastructure impacts) and occur in a greater array of peatland types (e.g. fens and swamps). Because fens are groundwater and surface flow driven, techniques to restore the hydrology of fens are generally more complicated than bogs. Restoring a greater variety of peatland types on a large‐scale basis (>10 ha) commands new techniques for reestablishing a broader array of plants other than Sphagnum spp., including non‐ Sphagnum mosses, sedges, nonericaceous shrubs, and trees. The rationale for restoring peatlands has expanded to include legal requirements, wetland mitigation and banking, climate mitigation, water quality, and as part of responsible ecosystem management for industry or society. In the past 25 years, peatland restoration in NA has evolved from (1) trial and error to a more empirically based scientific approach, (2) small site‐specific experiments to landscape‐scale restoration (e.g. hydrological connectivity, ecological fragmentation), and (3) individual stakeholder (academic) to multiple stakeholders across jurisdictional boundaries (private, local, and regional governmental agencies, NGOs , and so on). However, many research gaps still exist that must be addressed to enhance our ability to restore peatlands successfully.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".