The effects of black spruce fuel management on surface fuel condition and peat burn severity in an experimental fire
Bibliographic record
Abstract
In the boreal plains ecozone, black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) peatlands can represent large parts of the expanding wildland–urban interface (WUI) and wildland–industry interface (WII). The boreal plains wildfire regime is predicted to increase in areal extent and intensity, amplifying the need for wildfire management to protect the WUI and WII. Forested peatland ecosystems can burn at high intensity and present challenges for wildfire managers such as severe smouldering combustion and large carbon loss. Fuel management techniques such as mulching treatments (converting surface and canopy fuel to a masticated fuelbed) can be applied to black spruce peatlands, yet the impact on fuel load, condition, and peat burn severity is unclear. Using observations from an experimental fire, we found that a mulch-thinning fuel treatment could reduce peat depth of burn. However, where peat bulk density was increased by compaction, this led to an increased peat combustion carbon loss relative to the control. Furthermore, near-total combustion of the mulch layer resulted in significantly more surface fuel carbon emission from thinned and stripped fuel-treated areas compared with the control. We argue that although fuel treatment may benefit smouldering combustion suppression efforts, surface fuel carbon loss should be considered before treatments are implemented on a large scale.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".