Control of the multimillennial wildfire size in boreal North America by spring climatic conditions
Bibliographic record
Abstract
Wildfire activity in North American boreal forests increased during the last decades of the 20th century, partly owing to ongoing human-caused climatic changes. How these changes affect regional fire regimes (annual area burned, seasonality, and number, size, and severity of fires) remains uncertain as data available to explore fire-climate-vegetation interactions have limited temporal depth. Here we present a Holocene reconstruction of fire regime, combining lacustrine charcoal analyses with past drought and fire-season length simulations to elucidate the mechanisms linking long-term fire regime and climatic changes. We decomposed fire regime into fire frequency (FF) and biomass burned (BB) and recombined these into a new index to assess fire size (FS) fluctuations. Results indicated that an earlier termination of the fire season, due to decreasing summer radiative insolation and increasing precipitation over the last 7.0 ky, induced a sharp decrease in FF and BB ca. 3.0 kyBP toward the present. In contrast, a progressive increase of FS was recorded, which is most likely related to a gradual increase in temperatures during the spring fire season. Continuing climatic warming could lead to a change in the fire regime toward larger spring wildfires in eastern boreal North America.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".