Soil heating during wildfires and prescribed burns: a global evaluation
Bibliographic record
Abstract
Background Fires can alter soil properties via downward heat transfer. Numerous studies have examined effects of wildfires and prescribed burns on soils, yet knowledge of the soil temperatures and durations reached is limited. This can lead to erroneous assumptions regarding fire impacts, especially when laboratory heating results are extrapolated to field conditions. Aims and Methods We compiled new and published data on maximum temperatures and heating durations for mineral soils during wildfires and prescribed burns in forests, shrublands and grasslands around the globe, and compared these to data from laboratory heating experiments. Key results Most fires heated only the uppermost centimetres of the soil, rarely exceeding 300°C below 0.5 cm depth. Where 300°C was exceeded at the soil surface, heat pulses were shorter (<500 s) than those often applied in laboratory studies (30 min to 1 h). The highest near-surface temperatures occurred in shrubland wildfires (~964ºC), and longest heating durations in forests with deep duff layers (>3 h above 60°C). Conclusions and Implications Most fires, except in deep smouldering fuels, generate short and shallow soil heating. Laboratory studies with long heating durations rarely represent field conditions. When investigating fire effects on soil, inclusion of shallow near-surface layer samples is recommended.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".