Growth responses to thinning and pruning in <i>Eucalyptus globulus</i>, <i>Eucalyptus nitens</i>, and <i>Eucalyptus grandis</i> plantations in southeastern Australia
Bibliographic record
Abstract
Growth responses to pruning or thinning are well documented but their interactions are not, even though they are sometimes performed simultaneously. Growth responses to thinning and pruning were examined in nine plantation silvicultural experiments at five sites in southeastern mainland Australia. The species studied were Eucalyptus globulus Labill., Eucalyptus nitens (Deane and Maiden) Maiden, and Eucalyptus grandis Hill ex Maiden. Thinning from about 1100–1300 trees·ha–1 to about 300 or 500 trees·ha–1 at either age 3–4 years or 7–10 years increased the volume of sawlog crop trees in all species. Multiple lift pruning to 6.5 m height on the sawlog crop trees that retained at least 70% of the live crown length at any lift significantly reduced tree growth at only one of the six site–species combinations where both thinning and pruning were studied. And here, thinning interacted with pruning such that the pruning effects were not significant in unthinned stands because only shaded and inefficient foliage was removed. This study shows that thinning and pruning can interact to influence sawlog crop tree growth and this interaction is influenced by site.
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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".