Resistance of the boreal forest to high burn rates
Bibliographic record
Abstract
Boreal ecosystems and their large carbon stocks are strongly shaped by extensive wildfires. Coupling climate projections with records of area burned during the last 3 decades across the North American boreal zone suggests that area burned will increase by 30-500% by the end of the 21st century, with a cascading effect on ecosystem dynamics and on the boreal carbon balance. Fire size and the frequency of large-fire years are both expected to increase. However, how fire size and time since previous fire will influence future burn rates is poorly understood, mostly because of incomplete records of past fire overlaps. Here, we reconstruct the length of overlapping fires along a 190-km-long transect during the last 200 y in one of the most fire-prone boreal regions of North America to document how fire size and time since previous fire will influence future fire recurrence. We provide direct field evidence that extreme burn rates can be sustained by a few occasional droughts triggering immense fires. However, we also show that the most fire-prone areas of the North American boreal forest are resistant to high burn rates because of overabundant young forest stands, thereby creating a fuel-mediated negative feedback on fire activity. These findings will help refine projections of fire effect on boreal ecosystems and their large carbon stocks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".