Is regulated even-aged management the right strategy for the Canadian boreal forest?
Bibliographic record
Abstract
Over the past decade, there has been an increasing interest in the development of forest management approaches that are based on an understanding of historical natural disturbance dynamics. The rationale for such an approach is that management to favour landscape compositions and stand structures similar to those of natural ecosystems should also maintain biological diversity and essential ecological functions. In fire-dominated landscapes, this approach is possible only if current and future fire frequencies are sufficiently low, in comparison to pre-industrial fire frequency, that we can substitute fire with forest management. I address this question by comparing current and future fire frequency to historical reconstruction of fire frequency from studies in the Canadian boreal forest. Current and simulated future fire frequencies using 2× and 3×CO 2 scenarios are lower than the historical fire frequency for most sites, suggesting that forest management could potentially be used to recreate the forest age structure of fire-controlled pre-industrial landscapes. Current even-aged management, however, tends to reduce forest variability: for example, fully regulated, even-aged management will tend to truncate the natural forest stand age distribution and eliminate overmature and old-growth forests from the landscape. The development of silvicultural techniques that maintain a spectrum of forest compositions and structures at different scales in the landscape is one avenue to maintain this variability. Key words: boreal forest, even aged management, fire regime, old-growth forests, climate change, partial cutting
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".