Using silvicultural practices to regulate competition, resource availability, and growing conditions for <i>Pinus palustris</i> seedlings underplanted in <i>Pinus taeda</i> forests
Bibliographic record
Abstract
In the southeastern United States, many forest managers are interested in restoring longleaf pine (Pinus palustris Mill.) to upland sites that currently support loblolly pine (Pinus taeda L.). We quantified the effects of four canopy treatments (uncut Control; MedBA, harvest to 9 m2·ha−1; LowBA, harvest to 5 m2·ha−1; and Clearcut) and three cultural treatments (NT, no treatment; H, herbicide release of longleaf pine seedlings; and H+F, herbicide release plus fertilization) on resource availability and growing conditions in relation to longleaf pine seedling response for 3 years. Harvesting treatments reduced competition from canopy trees but resulted in greater abundance of understory vegetation. Harvesting shifted the interception of light from the canopy to the subcanopy vegetation layer; however, total light availability at the forest floor increased with the intensity of canopy removal. Soil moisture was not affected by harvesting or by the cultural treatments. Foliar nutrient concentrations (N, P, and K) of longleaf pine seedlings generally increased with the intensity of the harvest treatment. Of the plant resources measured, we found that light was most strongly correlated with longleaf pine seedling growth and that incorporating the interception of light by subcanopy vegetation improved the relationship over that of canopy light transmittance alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".