Impact of Climate Change on Forest Fire Severity and Consequences for Carbon Stocks in Boreal Forest Stands of Quebec, Canada: a Synthesis
Bibliographic record
Abstract
The global boreal forests comprise large stocks of organic carbon that vary with climate and fire regimes. Global warming is likely to influence several aspects of fire and cause shifts in carbon sequestration patterns. Fire severity or forest floor depth of burn is one important aspect that influences both carbon emission during combustion as well as post-fire ecosystem regeneration. Numerous publications on projections of future area burned exist, whereas scenarios on twenty-first century fire severity are more scarce, and the stand-typical response to severe fire weather is rarely taken into account. This paper aims to synthesize knowledge on boreal forest carbon stocks in relation to changes in fire severity for Quebec, Canada. Besides warming, this region may be subjected to an important increase in future precipitation. Future fire severity and area burned may well increase as fire weather will be drier, especially near the end of the twenty-first century. Moreover, the fire season peak may shift towards the late summer. Intense burning will favour tree cover development while the forest floor carbon stock may become less important. As a result, total Quebec boreal carbon sequestration may diminish. The development of dynamic vegetation models may improve scenarios on twenty-first century changes in carbon sequestration driven by climate change and fire severity and frequency effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".