Artificial tip-up mounds influence tree seedling composition in a managed northern hardwood forest
Bibliographic record
Abstract
Silvicultural regeneration methods focus on manipulating the forest canopy, but success can depend on the use of site preparation to control competing vegetation, including the density of advance regeneration, and create suitable microsite conditions for germination and seedling establishment. Tip-up mounds are known to provide favorable conditions for some tree species, but the creation of tip-up mounds as a method of site preparation has scarcely been investigated. We assessed effects of artificial tip-up mounds on tree seedling composition across a gradient of regeneration methods and residual overstory densities 2–4 years post-implementation. We found that tree seedling communities on mounds in some treatments were compositionally distinct from untreated reference plots. However, no tree species exhibited a strong affinity for mounds when analyzed independently from the regeneration method, and much of the difference in composition was associated with lower dominance of maples ( Acer spp. L.) on mounds. As maples are strong competitors in forests regenerated with selection systems, reduced maple competition on artificial mounds could advantage desired under-represented species and aid in natural regeneration over time. Therefore, in stands where promoting tree species diversity is desirable, implementing artificial tip-up mounds as part of a long-term strategy may be beneficial.
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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 | 0.001 |
| 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".