Forest management is driving the eastern North American boreal forest outside its natural range of variability
Bibliographic record
Abstract
Fire is fundamental to the natural dynamics of the North American boreal forest. It is therefore often suggested that the impacts of anthropogenic disturbances (eg logging) on a managed landscape are attenuated if the patterns and processes created by these events resemble those of natural disturbances (eg fire). To provide forest management guidelines, we investigate the long‐term variability in the mean fire interval (MFI) of a boreal landscape in eastern North America, as reconstructed from lacustrine (lake‐associated) sedimentary charcoal. We translate the natural variability in MFI into a range of landscape age structures, using a simple modeling approach. Although using the array of possible forest age structures provides managers with some flexibility, an assessment of the current state of the landscape suggests that logging has already caused a shift in the age‐class distribution toward a stronger representation of young stands with a concurrent decrease in old‐growth stands. Logging is indeed quickly forcing the studied landscape outside of its long‐term natural range of variability, implying that substantial changes in management practices are required, if we collectively decide to maintain these fundamental attributes of the boreal forest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".