Mapping smouldering fire potential in boreal peatlands and assessing interactions with the wildland–human interface in Alberta, Canada
Bibliographic record
Abstract
Treed peatlands exhibit both crown and smouldering fire potential; however, neither are included in Canadian wildfire management models and, as such, they are not formally represented in management decision-making. The lack of smouldering fire risk assessment is a critical research gap as these fires can represent heavy resource draws and are predominant sources of smoke, air pollutants and atmospheric carbon. Here, for the first time, we combine existing knowledge of the controls on smouldering peat fire with expert opinion-based weightings through a multi-criteria decision analysis, to map the smouldering fire potential (i.e. hazard) of treed peatlands in the Boreal Plains, Alberta, Canada. We find that smouldering potential varies considerably between treed peatlands and that areas of sparser peatland coverage may contain high smouldering-potential peatlands. Further, we find that treed peatlands are a common feature in the wildland–human interface and that proportionally, the area of high smouldering potential is greater closer to roads compared with farther away. Our approach enables a quantitative measure of smouldering fire potential and evidences the need to incorporate peatland–wildfire interactions into wildfire management operations. We suggest that similar frameworks could be used in other peatland dominated regions as part of smouldering fire risk assessments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".