Impact of wildfire on the thermal behavior of northern peatlands: Observations and model simulations
Bibliographic record
Abstract
Wildfire represents the single largest disturbance to the ecohydrological function of northern peatlands. Alterations to peatland thermal behavior as a result of wildfire will modify the carbon balance of these important long‐term global carbon stores and regulate post‐fire ecosystem recovery. We simulate the 3‐D thermal behavior of a peatland that has been disturbed by wildfire to identify how changes in peat temperatures emerge from changes to the surface energy balance and peat thermal properties. Peat temperatures are simulated within two adjacent peatlands, one area having burned 4 years previously, the second which has been wildfire‐free for 75 years. We demonstrate that there is only a small alteration to the thermal response inSphagnum fuscum hummocks that are not severely burnt within the wildfire. In contrast, wildfire produces important changes to the energy balance of Sphagnumhollows. A large reduction in the latent heat flux post‐fire increases surface temperatures by up to 30°C during daytime summer conditions. However, temperatures through the peat profile are insensitive to these increases in surface temperature. The low surface moisture content of near‐surface peat insulates the profile from these higher temperatures and, at depths below 0.015 m, only small differences are identifiable between burned and unburned hollow temperatures. Nevertheless, we argue that these alterations to near‐surface temperatures and evaporation rates likely substantially influence the thermal and hydrological conditions post‐wildfire, impacting the peatland recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".