Long-term Impacts of Silvicultural Treatments on the Regeneration of American Beech in Response to Beech Bark Disease Outbreaks
Bibliographic record
Abstract
Following the insurgence of beech bark disease, forest managers seek to reduce the formation of American beech thickets in the understory while promoting the establishment of other desirable hardwoods. High American beech component (>10%) in hardwood stands of Haliburton Forest, Ontario is currently facing rates as high as 90% mortality of the beech canopy. In addition, beech sucker development in the understory impedes the growth of desirable hardwoods and decreases stand diversity to the detriment of ecological values. Analysis indicated no significant difference in beech regeneration density amongst the treatments; however, there were significant differences in beech density over time (p = <0.001). The study also found that intensely harvested stands contained greater species diversity five years later. The study concluded that the most intensive harvest methods had the greatest effect on reducing beech density and promoting a diverse cohort of hardwoods.
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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 | 0.000 | 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) | 0.001 | 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".