Altering successional trends in oak forests: 19 year experimental results of low- and moderate-intensity silvicultural treatments
Bibliographic record
Abstract
Intensive silvicultural treatments can sometimes prevent the conversion of an oak (Quercus spp.) forest to a forest composed of mesophytic competitors following harvest, but the required labor is a disincentive for many private landowners. In this study, shelterwood removal, commercial clear-cutting, understory control, and oak underplanting were conducted on mesic and dry–mesic sites in southwestern Wisconsin to evaluate the effect of these treatments on forest composition and to identify the least intensive combination needed for successful oak regeneration. Commercial clear-cutting, with or without prior herbicide spray of low vegetation and oak underplanting, resulted in nearly complete dominance by a wide array of non-oak species on both mesic and dry–mesic sites. In contrast, 153–903 ha–1of the oaks that were underplanted on shelterwood – understory removal plots successfully achieved dominant or codominant status by age 19. Control of tall understory saplings was essential for successful oak regeneration on both sites. On the mesic site, oak underplanting was an additional necessary treatment, whereas natural regeneration was adequate in shelterwood plots on the dry–mesic site. The study suggests that successful oak regeneration can be obtained on productive sites in this region after a single application of a moderately intense silvicultural treatment, although the effort required for understory control may still be an obstacle to widespread application without external incentives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".