Estimating the heat transfer to an organic soil surface during crown fire
Bibliographic record
Abstract
The Peatland Smouldering and Ignition (PSI) model was developed to quantify the heat transfer from a wildfire to an organic soil or moss surface in a Sphagnum–black spruce peatland. The Canadian Fire Behaviour Prediction system was used as a basis for the relationship between wind speed and rate of spread. Convection, conduction, and radiation processes were modelled before and during the arrival of the flaming front. The net heat flux to the soil from fire varied between 1.1 and 8.6 MJ m–2, with moderate-intensity fires transferring more energy to the surface compared with higher-intensity fires under higher winds. Radiative heat transfer to the soil surface both before the fire’s arrival and within the flaming front were the primary mechanisms of energy gain to the peatland surface. The role of convective and conductive cooling was no greater than 30% of gross energy gain. Peatland surface ignition in hummock and hollow microforms was modelled under normal and drought conditions. Hollow microforms dried out significantly during the course of a summer and increased their ignition vulnerability. Small-scale changes in slope and aspect of the peatland surface increased the amount of heat transferred by radiation by up to 30%, allowing some areas of higher soil moisture content to ignite. While no direct model validation is available, model outputs showing the preferential combustion of lichen and feathermoss, and the lack of ignition in Sphagnum in all but the most severe drought generally mimic observed ignitions patterns. The modelled peak of net energy input to the surface occurred at moderate wind speeds, suggesting that high-intensity fires do not necessarily lead to greater energy transfer and risk of smouldering combustion.
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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".