Ecosystem Management of Eastern Canadian Boreal Forests: Potential Impacts on Wind Damage
Bibliographic record
Abstract
Research Highlights: Windthrow can interfere significantly with ecosystem management practices. In some cases, their goal could still be reached but this may prove more complex in other cases, like the partial cutting of old-growth stands. In situations where windthrow is common without any human intervention, the use of partial cutting to maintain some stand structures may lead to a feedback loop leading to additional windthrow. Background and Objectives: Forest ecosystem management using natural disturbances as a template has become the management paradigm in many regions. Most of the time, the focus is on fire regime and effects. However, windthrow can be common in some places or can interfere with practices implemented in an ecosystem management strategy. This paper looks at interactions between ecosystem management and windthrow. Materials and Methods: The paper builds on three case studies looking at various elements that could be part of ecosystem management strategies. The first one looks at the impact of green tree retention, while the second one looks at the impact of reducing the size and dispersing clearcuts, and the last one examines the impact of a range of cutting practices in irregular old-growth stands. Results: Green tree retention leads to increased windthrow, especially when applied within mature even-aged stands. Reducing the size of clearcuts and dispersing them over the landscape also involves substantial windthrow along edges. Partial cutting in old-growth stands can lead to relatively high mortality, but part of it is not necessarily related to wind since it occurs as standing dead trees. Differences in the amount of damage with tree size and species have been found and could be used to reduce wind damage. Conclusions: Approaches to minimize wind damage in ecosystem management can be designed using existing knowledge. However, using windthrow as a template to design management strategies would prove more complex.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".