Success Factors for Experimental Partial Harvesting in Unmanaged Boreal Forest: 10-Year Stand Yield Results
Bibliographic record
Abstract
Over the past two decades, partial harvesting has been increasingly used in boreal forests as an alternative to clearcutting to promote irregular stand structures and maintain a balance between biodiversity preservation and continued timber production. However, relatively little is still known about the silvicultural potential of partial harvesting in Canada's boreal forest, especially in areas prone to organic matter accumulation (paludification), and most prior research has focused on biodiversity responses. In this study, we assess the effects of partial harvesting on stand development (recruitment, growth, and mortality) ten years after harvesting in previously unmanaged black spruce stands and quantify its effectiveness in reducing the impacts on ecosystem structures. Our analyses revealed that pre-harvest stand structure and site characteristics, especially initial basal area, sapling density, tree diameter, and organic layer thickness (OLT) were major factors involved in stand development ten years following these partial harvesting treatments. Depending on pre-harvest structure and site characteristics, partial harvesting can result in either an increase in post-harvest tree recruitment and growth or a loss of stand volume because of standing tree mortality. To increase the chances of partial harvesting success in ensuring an increase in decennial stand yield after harvest in black spruce forest stands, sites prone to paludification (i.e., where OLT >17 cm) should be left unharvested. This study illustrates the importance of taking into account pre-existing structure and site characteristics in the selection of management strategies to maximize the potential of partial harvesting to achieve sustainable forest management in black spruce stands.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 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".