Gaps in a gappy forest: plant resources, longleaf pine regeneration, and understory response to tree removal in longleaf pine savannas
Bibliographic record
Abstract
Resource availability and planted longleaf pine (Pinus palustris Mill.) seedling and understory vegetation response within and among three sizes of experimentally created canopy gaps (0.11, 0.41, 1.63 ha) in a mature longleaf pine savanna were investigated for 2 years. Longleaf pine seedlings and understory vegetation showed increased growth in gaps created by tree removal. Longleaf pine seedling growth within gaps was maximized approximately 18 m from the uncut savanna. Increased longleaf pine seedling survival under the uncut savanna canopy observed after the first year suggests that the overstory may facilitate establishment of longleaf pine seedlings rather than reduce survival through competition. Despite the relative openness of the uncut longleaf pine forest, light quantity was increased by tree removal. Light was also the resource most strongly correlated with seedling and understory vegetation growth. Although net N mineralization was correlated to seedling response, the amount of variation explained was low relative to light. Belowground (root) gaps were not strong, in part because of non-pine understory roots increasing in biomass following tree removal. These results suggest that regeneration of longleaf pine may be maximized within gap sizes as small as approximately 0.10 ha, due largely to increases in light availability.
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".