Shallow peat is most vulnerable to high peat burn severity during wildfire
Bibliographic record
Abstract
Abstract Peatlands typically act as carbon sinks, however, increasing wildfire severity and annual area burned may challenge this carbon sink status. Whilst most peat resistance to wildfire and drought research is based on deep peatlands that rarely lose their water table below the peat profile, shallow peatlands and peat deposits may be most vulnerable to high peat burn severity and extensive carbon loss. To examine the role of pre-fire peat depth on peat burn severity, we measured the depth of burn (DOB) in peat of varying depths (0.1–1.6 m) within a rock barrens landscape. We found that DOB (0–0.4 m) decreased with increasing pre-fire peat depth, and that there was a strong correlation between the percent of the profile that burned and pre-fire peat depth. Breakpoint analysis indicates a threshold depth of 0.66 m where deeper peat deposits experienced little impact of wildfire, whereas shallower peat typically experienced high peat burn severity (median percent burned = 2.2 and 65.1, respectively). This threshold also corresponded to the loss of the water table in some nearby unburned peatlands, where water table drawdown rates were greater in shallower peat. We suggest that peat depth may control peat burn severity through feedbacks that regulate water table drawdown. As such, we argue that the identification of a critical peat depth threshold could have important implications for wildfire management and peatland restoration aiming to protect vulnerable carbon stores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".