Converging and diverging burn rates in North American boreal forests from the Little Ice Age to the present
Bibliographic record
Abstract
Warning This article contains terms, descriptions, and opinions used for historical context that may be culturally sensitive for some readers. Background Understanding drivers of boreal forest dynamics supports adaptation strategies in the context of climate change. Aims We aimed to understand how burn rates varied since the early 1700s in North American boreal forests. Methods We used 16 fire-history study sites distributed across such forests and investigated variation in burn rates for the historical period spanning 1700–1990. These were benchmarked against recent burn rates estimated for the modern period spanning 1980–2020 using various data sources. Key results Burn rates during the historical period for most sites showed a declining trend, particularly during the early to mid 1900s. Compared to the historical period, the modern period showed less variable and lower burn rates across sites. Mean burn rates during the modern period presented divergent trends among eastern versus northwestern sites, with increasing trends in mean burn rates in most northwestern North American sites. Conclusions The synchronicity of trends suggests that large spatial patterns of atmospheric conditions drove burn rates in addition to regional changes in land use like fire exclusion and suppression. Implications Low burn rates in eastern Canadian boreal forests may continue unless climate change overrides the capacity to suppress fire.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".