Keeping logs on the past: Log driving tells the story of fire regimes in pine forests of eastern Canada
Bibliographic record
Abstract
Abstract In North America, forest ecosystems have changed drastically since European settlement due to logging, land‐use changes, and altered disturbance regimes. For example, red and white pine stands declined significantly in the last three centuries, and this decline was attributed to their extensive harvesting during settlement. Human‐induced changes in fire regime is another probable cause of pine forests' decline that has gained attention in the last decades. However, the study of red and white pine forests can be challenging, because few pre‐settlement pine forests remain today, as they were extensively harvested during the 19th century. During this extensive exploitation of pine forests, logs were transported via log driving, and many of them sunk to the bottom of lakes. These sinker logs represent an opportunity to study pre‐settlement pine forests and their natural disturbance regimes. The aim of this research was to reconstruct fire regimes from the pre‐settlement period to late 20th century (1700–1970) in eastern Canadian pine forests. To achieve this goal, 1151 submerged logs were extracted from lakes in the Témiscamingue region (Québec), 60 of which exhibited fire scars. We built a reference chronology using 140 living pines to cross‐date 81 scars and were able to reconstruct fire activity since 1717. We then modeled the relative probability of fire occurrence across settlement periods using a Bayesian approach. Our results showed that the probability of fire occurrence almost doubled following the beginning of settlement (1840), highlighting the impact of intensified logging and land conversion on fire frequency. Our study is among the first to use sinker logs and a Bayesian approach to reconstruct and model preindustrial fire regimes in pine forests. This new knowledge is crucial to develop sustainable forest management practices and conservation strategies in red and white pine forests in North America.
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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 | 0.000 |
| 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 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".