Hydrological controls on deep burning in a northern forested peatland
Bibliographic record
Abstract
Abstract While previous boreal peatland wildfire research has generally reported average organic soil burn depths ranging from 0.05 to 0.20 m, here, we report on deep burning in a peatland in the Utikuma Complex forest fire (SWF‐060, ~90 000 ha, May 2011) in the sub‐humid climate of Alberta's Boreal Plains. Deep burning was prevalent at peatland margins, where average burn depths of 0.42 ± 0.02 m were fivefold greater than in the middle of the peatland. We examined adjacent unburned sections of the peatland to characterize the hydrological and hydrophysical conditions necessary to account for the observed burn depths. Our findings suggest that the peatland margin at this site represented a smouldering hotspot due to the effect of dynamic hydrological conditions on margin peat bulk density and moisture. Specifically, the coupling of dense peat (bulk density >100 kg m−3) and low peat moisture (m <250%) at the peatland margin allowed for severe smouldering to propagate deep into the peat profile. We estimated that carbon release from this margin ‘hotspot’ ranged from 10 to 85 kg C m−2 (mean = 27 kg C m−2), accounting for ~80% of the total soil carbon loss from the peatland during the wildfire. As such, we suggest that current estimations of peatland carbon loss from wildfires that exclude (and/or miss) these ‘hotspots’ are likely underestimating total carbon emissions from peatland wildfires. We conclude that assessments of natural and managed peatland vulnerability to wildfire should focus on identifying dense peat on the landscape that is vulnerable to drying. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".