Will climate change drive 21st century burn rates in Canadian boreal forest outside of its natural variability: collating global climate model experiments with sedimentary charcoal data
Bibliographic record
Abstract
Natural ecosystems have developed within ranges of conditions that can serve as references for setting conservation targets or assessing the current ecological integrity of managed ecosystems. Because of their climate determinism, forest fires are likely to have consequences that could exacerbate biophysical and socioeconomical vulnerabilities in the context of climate change. We evaluated future trends in fire activity under climate change in the eastern Canadian boreal forest and investigated whether these changes were included in the variability observed during the last 7000 years from sedimentary charcoal records from three lakes. Prediction of future annual area burned was made using simulated Monthly Drought Code data collected from an ensemble of 19 global climate model experiments. The increase in burn rate that is predicted for the end of the 21st century (0.45% year–1 with 95% confidence interval (0.32, 0.59) falls well within the long‐term past variability (0.37 to 0.90% year–1). Although our results suggest that the predicted change in burn rates per se will not move this ecosystem to new conditions, the effects of increasing fire incidence cumulated with current rates of clear‐cutting or other low‐retention types of harvesting, which still prevail in this region, remain preoccupying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".