Holocene variations of wildfire occurrence as a guide for sustainable management of the northeastern Canadian boreal forest
Bibliographic record
Abstract
Abstract Background Cumulative impacts of wildfires and forest harvesting can cause shifts from closed-crown forest to open woodland in boreal ecosystems. To lower the probability of occurrence of such catastrophic regime shifts, forest logging must decrease when fire frequency increases, so that the combined disturbance rate does not exceed the Holocene maximum. Knowing how climate warming will affect fire regimes is thus crucial to sustainably manage the forest. This study aimed to provide a guide to determine sustainable forest harvesting levels, by reconstructing the Holocene fire history at the northern limit of commercial forestry in Quebec using charcoal particles preserved in lake sediments. Methods Sediment cores were sampled from four lakes located close to the northern limit of commercial forestry in Quebec. The cores were sliced into consecutive 0.5 cm thick subsamples from which 1 cm3 was extracted to count and measure charcoal particles larger than 150 microns. Age-depth models were obtained for each core based on accelerator mass spectroscopy (AMS) radiocarbon dates. Holocene fire histories were reconstructed by combining charcoal counts and age-depth models to obtain charcoal accumulation rates and, after statistical treatment, long-term trends in fire occurrence (expressed as number of fires per 1000 years). Results Fire occurrence varied between the four studied sites, but fires generally occurred more often during warm and dry periods of the Holocene, especially during the Holocene Thermal Maximum (7000–3500 cal. BP), when fire occurrence was twice as high as at present. Conclusions The current fire regime in the study area is still within the natural range of variability observed over the Holocene. However, climatic conditions comparable to the Holocene Thermal Maximum could be reached within the next few decades, thus substantially reducing the amount of wood available to the forest industry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".