Effects of planting density and site quality on mean tree size and total stand growth of <i>Eucalyptus globulus</i> plantations
Bibliographic record
Abstract
The choice of planting density is a primary silvicultural decision in plantation management which considers the trade-off between individual tree size and total stand production, affecting the type, quantity and quality of products throughout the rotation. Trends in size and production with planting density are generally well known, however, less so is the interacting effect of site quality. Consequently, a case study in which basal area and basal area growth of Eucalyptus globulus Labill. plantations on five site qualities (122–435 m3·ha−1) planted at six densities (625 trees·ha−1, 4 m × 4 m; 833 trees·ha−1, 3 m × 4 m; 1000 trees·ha−1, 4 m × 2.5 m; 1250 trees·ha−1, 4 m × 2 m; 1667 trees·ha−1, 3 m × 2 m; and 2000 trees·ha−1, 3 m × 1.75 m) were used to investigate this interaction. As expected, both mean tree diameter of the whole stand and the basal area of the largest diameter 200 trees·ha−1 (D200 trees) were higher at lower planting densities, whereas whole stand basal area was greater at higher planting densities. However, there were no significant (P > 0.32) interactions between planting density and site quality for D200 or stand basal area, which contrasts with thinning responses in similar stands. This simplifies management considerations and suggests that trials at a given site quality may provide useful information about responses to planting density at other site qualities for the studied species.
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 | 0.001 | 0.001 |
| 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".