Impact of competition from coppicing stumps on the growth of retained trees differs in thinned <i>Eucalyptus globulus</i> and <i>Eucalyptus tricarpa</i> plantations in southeastern Australia
Bibliographic record
Abstract
Coppice growth on cut stumps in thinned Eucalyptus plantations has the potential to compete with and reduce the growth of retained sawlog crop trees (SCTs). This study examined to what extent SCT growth was reduced by coppice in two stands in southeastern Australia: (i) a Eucalyptus globulus Labill. plantation thinned at age 10 years and (ii) a slower growing Eucalyptus tricarpa L.A.S. Johnson & K. Hill (syn. Eucalyptus sideroxylon subsp. tricarpa L.A.S. Johnson) plantation thinned at age 62 years. After 5 years, thinning E. globulus from 850 to 400 trees·ha–1 increased the basal area of the largest diameter 200 SCTs·ha–1 (SCT200) by 11% when coppice was removed. There was no significant thinning response by SCT200 when coppice was retained. After 10 years, thinning E. tricarpa from about 600 to 100 trees·ha–1 increased the basal area of the largest diameter 100 SCTs trees·ha–1 (SCT100) by about 10% whether coppice was removed or not. At the time of measurement, coppice contributed 17% and 36% of stand sapwood area in thinned E. globulus and E. tricarpa treatments, respectively, and possibly competed with SCTs for water. This study shows the significant competitive effect that coppice can have in thinned eucalypt plantations and the importance of coppice management to the growth of retained trees.
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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".