A new silvicultural approach to the management of uneven-aged Northern hardwoods: frequent low-intensity harvesting
Bibliographic record
Abstract
We report a new silvicultural approach that is well suited for the management of uneven-aged forests in which timber production is an important objective. The approach recognizes two main components in the stand, i.e. a fiber production component, which provides veneer/sawlog quality products from the high-quality trees (HQT), and an ecological component, which contributes to the overall ecosystem functioning through the lower value stems. The objective of the study was to verify if it is possible to sustainably harvest only HQT in northern hardwood (NH) and thereby produce a viable alternative to high-grading the stands. To do so, a simple stand growth simulator, based on empirical growth rates of HQT in Sugar Maple/Yellow Birch stands in southwestern Quebec, was combined with an optimization tool. The optimization parameters aimed to identify possible tree marking regimes (TMRs) under 10-year rotation partial cutting, which would ensure that the basal area of HQT was maintained for 40 years. Results suggest that sustainability is achievable starting from very different initial stand structures and the application of a wide range of alternative TMRs. We argue that this new approach is one way to apply emerging concepts in forest management, such as ecological integrity, attempts to emulate natural disturbance regimes and provides new possibilities managing for resilience and for adaptation to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".