Performance of northern red oak (Quercus rubra) underplantings under five management regimes and across existing environmental gradients
Bibliographic record
Abstract
Oaks (Quercus sp.) are dominant throughout much of the eastern broadleaf forests of the United States and are of great ecological, economical, and cultural value. Despite their prevalence in the overstory, many sites are lacking in the advanced reproduction necessary for its regeneration in future stands. This dilemma has merited much research, but widely applicable and consistently successful methods for regenerating oak remain elusive. This study examines the response of northern red oak (Q. rubra) underplantings to forest management regimes across the environmental gradients of physiographic province, aspect, and fencing levels. Management regimes included 1) control sites, 2) a single prescribed burn, 3) repeat prescribed burns 5) diameter-limit cuts and 4) the seedcut of a shelterwood harvest. Physiographic provinces included the Ridge and Valley and Appalachian Plateau. The direct relationships between seedling performance and light as well as light and stand structure are also addressed. Seedling growth and survival are found to be driven by a combination of factors. The interaction of physiographic province and management regime exerted a significant influence on seedling survival. While high survival rates were present on sites receiving diameter-limit cuts and shelterwood cuts regardless of province, underplantings experienced a more dramatic drop in survival on single burn and control sited in the Appalachian Plateau than Ridge and Valley. Sapling density appears as stronger limiting factor of light levels on the more mesic Appalachian Plateau sites, and low survival on these sites reflected this. In contrast, the less dense sapling layer and generally higher light levels of the Ridge and Valley enabled underplanted seedlings to better persist here in the absence of overstory removal. The interaction of fencing and physiographic province was significant as well. Deer, were more problematic in this province, and the potential for herbivory to interfere with seedling response to increased resources was evident. Ultimately, the relatively brief duration of the study limits conclusions on the future of these underplantings, but results reinforce the importance of regional differences in forest composition and structure in determining the effectiveness of prescriptions. An awareness of this is particularly important when considering underplanting.
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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".