Growth response following green crown pruning in plantation-grown <i>Eucalyptus pilularis</i> and <i>Eucalyptus cloeziana</i>
Bibliographic record
Abstract
Pruning of live branches is performed to enhance wood quality in plantation trees. Stem, branch and wood properties may be altered following pruning from below, but the extent and duration of such effects are unknown for many plantation Eucalyptus species. The effect of 0%, 20%, 50%, and 70% green crown length removal on growth and wood quality was examined in 3.5-year-old plantation-grown Eucalyptus pilularis Sm. (blackbutt) and Eucalyptus cloeziana F. Muell. (Gympie messmate) trees on two subtropical sites in eastern Australia. Growth increment of the stem at breast height was not affected by 20% crown removal. However, stem increment at breast height was reduced for up to 8 months with 50% crown removal and up to 12 months with 70% crown removal. Pruning had no impact on height growth. However, the height of trees pruned to 70%, relative to the height of the surrounding unpruned trees, was temporarily reduced 12 months after pruning in both species. Stem form, stem taper, wood density, and residual branch growth above the pruned zone were unaffected by pruning. Owing to the fast vertical crown expansion, trees were able to rapidly compensate for leaf area removal without long-term reductions in stem growth. The implications for management operations utilizing pruning as a means of enhancing timber quality are discussed.
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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) | 0.000 | 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".