Forest type drives the response of boreal forested peatlands to wildfire: a simulation study
Bibliographic record
Abstract
Boreal black spruce forests contribute to climate change mitigation by accumulating large amounts of carbon (C) in moss-derived peat. When left undisturbed, a thick peat layer can inhibit tree growth, and this trade-off between peat and tree biomass can have implications on the forest C dynamics. Similarly, wildfire severity and frequency can modify C accumulation patterns, but this impact remains poorly documented. We used the Carbon Budget Model of the Canadian Forest Sector version 3 (CBM-CFS3) to explore over a 400-year simulation period, the effects of high-severity fire (HSF) and low-severity fire (LSF) on C dynamics of two forest types (black spruce–Sphagnum (BSSP) and black spruce–feathermoss (BSFM)). We found that total C stocks increased to higher levels after LSF than after HSF in BSSP due to peat accumulation. Conversely, in BSFM, HSF resulted in greater C storage than LSF due to tree biomass. The tree component is key to the rapid recovery of C pools in both BSSP and BSFM forests specifically after HSF, while mosses maintain C sinks over the long term. This study suggests that a good characterization of forest type is key to better predictions of the effects of a change in fire regime on ecosystem C dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".