Data and codes to reproduce analysis in "Six year efficacy of silvicultural treatments to control American beech regeneration in stands affected by beech bark disease in Ontario, Canada"
Bibliographic record
Abstract
Beech regeneration density has been a difficult challenge for forest managers in north-east North America’s temperate hardwoods. Beech regeneration can outcompete sugar maple and persist in the understory for long periods of time causing gradual reductions in sugar maple overstory abundance. This is especially problematic when combined with the ongoing threat of beech bark disease which causes a very high mortality rate in mature beech. To combat the threat of high levels of beech regeneration, we established an experimental network to investigate the impacts of three beech control methods (control untended, basal bark application of triclopyr, and mechanical removal with brush saw) at two sites with two different timing applications (5 years post single-tree selection harvest, and concurrent with a uniform shelterwood harvest) on beech and sugar maple regeneration density. We found that while both brush saw and basal bark could reduce large beech regeneration at both sites smaller beech regeneration density increased back to pre-tending levels in most other treatment combinations. Medium sugar maple regeneration did increase in the concurrent tending site over six years but regeneration density in all size classes of maple regeneration were similar across all three beech control methods. We found that while these beech control methods can temporarily reduce beech regeneration density, sugar maple was unable to take advantage of any increased growing space on our sites. We conclude that control of beech regeneration alone is not sufficient to stimulate sugar maple regeneration in Central Ontario 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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.182 | 0.039 |
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".