Thinned slash pine (<i>Pinus elliottii</i>) stand response to midrotation vegetation control and fertilization on flatwoods sites
Bibliographic record
Abstract
Midrotation management, including vegetation control and fertilization, can improve growth and yield of southern pine stands. This study investigated the 12-year response of slash pine ( Pinus elliottii), thinned at midrotation to additional treatments of herbicide, fertilization, liming (one site), and combinations of these treatments in the Coastal Plain region of Georgia, USA. There was a significant treatment × stand age interaction observed at the Ware County site for height ( p < 0.001), diameter at breast height (dbh) ( p < 0.001), and individual tree green weight ( p < 0.001). The herbicide + NPK and herbicide treatments resulted in the most consistent growth improvements 0 to 4 years post-treatment compared to the untreated control. The lime + NPK and herbicide treatment resulted in the longest lived (4–12 years) significantly improved growth response. Results revealed no tested treatments improved growth during the 12-year study duration at the Wayne County site. Results from this study suggest that thinned slash pine stands with diagnostics indicating midrotation fertilization or vegetation control may be beneficial do not always respond as expected to inputs. When stands are responsive, single application midrotation treatments can offer long-term growth improvements, yet growth response can differ depending on time since application.
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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".