Interactive effects of vegetation, soil moisture and bulk density on depth of burning of thick organic soils
Bibliographic record
Abstract
The boreal biome is characterised by extensive wildfires that frequently burn into the thick organic soils found in many forests and wetlands. Previous studies investigating surface fuel consumption generally have not accounted for variation in the properties of organic soils or how this affects the severity of fuel consumption. We experimentally altered soil moisture profiles of peat monoliths collected from several vegetation types common in boreal bogs and used laboratory burn tests to examine the effects of depth-dependent variation in bulk density and moisture on depth of fuel consumption. Depth of burning ranged from 1 to 17 cm, comparable with observations following natural wildfires. Individually, fuel bulk density and moisture were unreliable predictors of depth of burning. However, they demonstrated a cumulative influence on the thermodynamics of downward combustion propagation. By modifying Van Wagner’s surface fuel consumption model to account for stratigraphic changes in fuel conditions, we were able to accurately predict the maximum depth of fuel consumption for most of the laboratory burn tests. This modified model for predicting the depth of surface fuel consumption in boreal ecosystems may provide a useful framework for informing wildland fire management activities and guiding future development of operational fire behaviour and carbon emission models.
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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 | 0.001 |
| 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 | 0.000 | 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".