Performance of planted spruce and natural regeneration after pre- and post-harvest spraying with glyphosate and partial cutting on an Ontario (Canada) boreal mixedwood site
Bibliographic record
Abstract
In the boreal mixedwood region of Canada, management for conifer regeneration after clearcutting generally involves mechanical site preparation, planting and aerial spraying with glyphosate to minimize post-planting competition from natural broadleaf regeneration. However, the resulting conifer plantations have economic and ecological disadvantages compared with mixedwood stands with healthy and productive broadleaf components. This study examined 10-year growth of planted spruce and natural regeneration after pre- and post-harvest spraying and partial cutting treatments on a boreal mixedwood site in northeastern Ontario, Canada. The treatments were as follows: (1) pre-harvest broadcast spraying with glyphosate to suppress trembling aspen (Populus tremuloides Michx.) regeneration, (2) clearcut (unsprayed) for broadleaf regeneration, (3) partial cut to suppress shade-intolerant vegetation, and (4) post-harvest broadcast spraying to promote conventional conifer plantations. Planted spruce trees were tallest in the pre-harvest spray treatment but had the largest basal diameter in the post-harvest spray treatment; neither of the differences was significant at 0.05. Total broadleaf regeneration density in the pre-harvest spray treatment was similar to that in the partial cut, but higher than that in the post-harvest spray treatment. Additional shade from greater amounts of shrubs and residual overstory trees in the partial cut treatment resulted in higher quality spruce trees than in the spray treatments; based on branch size, branch-free stem length and stem taper, wood quality was generally lowest in the post-harvest spray treatment. Pre-harvest spraying provided a better balance between growth and quality of planted spruce than either post-harvest spraying or partial cutting.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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 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".