Vegetation structure and fire weather influence variation in burn severity and fuel consumption during peatland wildfires
Bibliographic record
Abstract
Temperate peatland wildfires are of significant environmental concern but information on their environmental effects is lacking. We assessed variation in burn severity and fuel consumption within and between wildfires that burnt British moorlands in 2011 and 2012. We adapted the composite burn index (pCBI) to provide semi-quantitative estimates of burn severity. Pre- and post-fire surface (shrubs and graminoids) and ground (litter, moss, duff) fuel loads associated with large wildfires were assessed using destructive sampling and analysed using a generalised linear mixed model (GLMM). Consumption during wildfires was compared with published estimates of consumption during prescribed burns. Burn severity and fuel consumption were related to fire weather, assessed using the Canadian Fire Weather Index System (FWI System), and pre-fire vegetation type. pCBI varied 1.6 fold between, and up to 1.7 fold within, wildfires. pCBI was higher where moisture codes of the FWI System indicated drier fuels. Spatial variation in pre- and post-fire fuel load accounted for a substantial proportion of the variance in fuel loads. Average surface fuel consumption was a linear function of pre-fire fuel load. Average ground fuel combustion completeness could be predicted by the Buildup Index. Carbon release ranged between 0.36 and 1.00 kg C m −2 . The flammability of ground fuel layers may explain the higher C release-rates seen for wildfires in comparison to prescribed burns. Drier moorland community types appear to be at greater risk of severe burns than blanket-bog communities.
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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 | 0.001 | 0.001 |
| 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".