Natural Regeneration Following Partial and Clear-Cut Harvesting in Mature Aspen-Jack Pine Stands in Eastern Canada
Bibliographic record
Abstract
Over the last three decades, the ecological basis for the generalized use of even-aged silviculture in boreal forests has been increasingly challenged. In boreal mixed-wood landscapes, the diminishing proportion of conifers, to the benefit of intolerant hardwoods, has been a primary concern, coupled with the general rarefication of old-growth conifer-dominated stands. In this context, partial cutting, extended rotations and forest renewal techniques that eliminate or reduce regenerating hardwoods have been proposed as means of regaining greater conifer cover. As a result, experimentation and industrial application of various forms of both variable retention and partial harvesting are occurring across the commercial Canadian boreal forest. In this study, we compared the effects of two harvesting intensities, clearcutting and low-intensity partial cutting (removal of 25–31% of tree basal area), on hardwood and conifer regeneration levels 7–19 years following treatments in aspen (Populus tremuloides)-dominated stands and verified whether regeneration differences existed between micro-sites on and off machinery trails. The abundance of aspen regeneration increased with percent basal area removal and was positively correlated to the abundance of mature aspen prior to harvesting. The abundance of fir (Abies balsamea) regeneration after partial cutting was similar to controls and higher than after clear-cutting and was positively correlated with ground cover of mixed litter (i.e., mixture of needles and leaves) and negatively correlated with ground cover of broadleaf litter. These results suggest that it is possible in boreal mixed-woods to control aspen abundance and promote or maintain conifer regeneration through silvicultural treatments that limit canopy opening and promote mixed forest floor litter.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".