Six year efficacy of silvicultural treatments to control American beech regeneration in stands affected by beech bark disease in Ontario, Canada
Bibliographic record
Abstract
High beech regeneration density is a concern in northern shade tolerant hardwood forests. High densities of beech ( Fagus grandifolia Ehrh) regeneration can crowd out other desirable species, such as sugar maple ( Acer saccharum Marsh.), and jeopardize long-term sustainability since beech is under threat from beech bark disease ( Cryptococcus fagisuga/Neonectria spp. Complex). We examined the efficacy of three tending (no tending, brush saw, and basal bark herbicide) and two timing and harvesting (deferred 5 years post-single tree selection harvest, concurrent with uniform shelterwood harvest) treatments on reducing beech regeneration density and promoting sugar maple regeneration density over 6 years. Six years after tending, we found that large beech regeneration density was reduced, medium beech regeneration density had recovered to pre-tending levels in most treatments, and small beech regeneration density remained unaffected. Tending treatments had no effect on any size class of sugar maple regeneration density but the uniform shelterwood harvest promoted medium sugar maple density more than the single-tree selection harvest. Despite this increase, in all treatment combinations sugar maple regeneration densities remained below stocking targets. Our results suggest that while tending treatments can temporarily reduce beech regeneration densities, sugar maple is unable to take advantage of the increased growing space.
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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 | 0.001 | 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".