Peatland-fire interactions: A review of wildland fire feedbacks and interactions in Canadian boreal peatlands
Bibliographic record
Abstract
Boreal peatlands store a disproportionately large quantity of soil carbon (C) and play a critical role within the global C-climate system; however, with climatic warming, these C stores are at risk. Increased wildfire frequency and severity are expected to increase C loss from boreal peatlands, contributing to a shift from C sink to source. Here, we provide a comprehensive review of pre- and post-fire hydrological and ecological interactions that affect the likelihood of peatland burning, address the connections between peatland fires and the C-climate cycle, and provide a conceptual model of peatland processes as they relate to wildland fire, hydro-climate, and ecosystem change. Despite negative ecohydrological feedback mechanisms that may compensate for increased C loss initially, the cumulative effects of climatic warming, anthropogenic peatland fragmentation, and subsequent peatland drying will increase C loss to the atmosphere, driving a positive C feedback cycle. However, the extent to which negative and positive feedbacks will compensate for one another and the timelines for each remains unclear. We suggest that a multi-disciplinary approach of combining process knowledge with remotely sensed data and ecohydrological and wildland fire models is essential for better understanding the role of boreal peatlands and wildland fire in the global climate system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".