Initial tree regeneration response to natural-disturbance-based silviculture in second-growth northern hardwood forests
Bibliographic record
Abstract
Northern hardwood stands in the Great Lakes region are often managed using single-tree selection, which generally favors regeneration of shade-tolerant species, especially sugar maple (Acer saccharum Marsh.) and may reduce regeneration of midtolerant and shade-intolerant species. These forests also tend to have lower microsite diversity than old-growth stands, which may negatively affect the regeneration of light-seeded species, including yellow birch (Betula alleghaniensis Britton). The objective of this research was to determine the initial effects of gap size and gap cleaning on tree regeneration in northern hardwood stands in northern Wisconsin, USA. The current study evaluated three gap sizes compared with a control. A gap-level cleaning treatment also examined effects of removal of advance regeneration and soil scarification. Postharvest seedling densities, especially shade-tolerant species, increased with increasing gap size. Rubus spp. increased significantly in the higher light conditions in these treatments. Density of yellow birch seedlings and saplings was low for all gap sizes but increased with removal of advance regeneration and soil scarification. These initial results underscore the challenges of using natural-disturbance-based treatments to increase the diversity of tree communities in second-growth forests and the importance of advance regeneration and seedbed conditions for increasing the abundance of historically important species.
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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) | 0.000 | 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 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".