Competitive responses of seedlings and understory plants in longleaf pine woodlands: separating canopy influences above and below ground
Bibliographic record
Abstract
A trenching study was used to investigate above- and below-ground competition in a longleaf pine ( Pinus palustris P. Mill.) woodland. Trenched and nontrenched plots were replicated in the woodland matrix, at gap edges, and in gap centers representing a range of overstory stocking. One-half of each plot received a herbicide treatment to remove the understory. We monitored pine survival and growth, understory productivity, light level (gap fraction), and soil resources. The overstory facilitated pine seedling survival. Pine seedling growth was reduced as overstory stocking increased. Reduced growth of seedlings was also observed in gaps when the understory was left intact. Understory plants competed with seedlings by filling the root gaps that developed as a result of overstory disturbance. Hardwood growth increased in gaps, owing to decreased belowground competition with adult pines, while growth of herbaceous plants and pine seedlings increased with light availability. Large overstory gaps are not required to initiate regeneration in longleaf pine woodlands. Retaining overstory dispersed throughout the stand but variable in density, through single-tree selection approaches, may be an alternative to gap-based approaches. This approach would allow for the fuel continuity needed to sustain the frequent fire required to maintain the diversity characteristic of this type of woodland.
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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) | 0.001 | 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".