Eastern boreal North American wildfire risk of the past 7000 years: A model‐data comparison
Bibliographic record
Abstract
We present here a 7000‐year wildfire reconstruction based on sedimentary charcoal series from five lakes located south of Hudson Bay in eastern boreal North America. The reconstruction shows a significant downward trend in the frequency of large fires from 0.0061 fire·yr −1 ca . 5000 cal yr BP to 0.0033 fire·yr −1 at present. Simulations of fire‐season climate based on UK Universities Global Atmospheric Modelling Programme output and reconstructions based on proxy data both indicate a shift toward increasing available moisture in the region between the mid‐Holocene and today. We infer that the diminishing trend in wildfire activity was ultimately caused by the steady orbitally driven reduction in summer insolation. Future higher temperatures not compensated for by significant precipitation increases will bring fire frequency back toward its upper limit, recorded between 6000 and 2000 cal yr BP.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".