Early-age and later-age thinning affects growth, dominance, and intraspecific competition in <i>Eucalyptus nitens</i> plantations
Bibliographic record
Abstract
High-intensity thinning treatments were applied to young Eucalyptus nitens (Deane and Maiden) Maiden plantations aged 6 (early-age thinning), 8, and 9 years (later-age thinning). Thinning treatments were an unthinned control plus final density levels that ranged from 100 to 600 trees/ha, representing between 14 and 72% of pretreatment stand basal area. Initial planting densities were between 1143 and 1430 trees/ha. Cumulative basal area increment was significantly reduced after both early- and later-age thinning if more than 50% of the standing basal area was removed. When select groups of trees in the thinning treatments were compared with the equivalent groups of trees in the unthinned control, there was a significant response to early-age thinning in the best 100-400 trees/ha and to later-age thinning for the best 100-600 trees/ha. Height increment was not affected by thinning. However, mean live crown ratio increased with time in thinned treatments. Dominant and codominant trees showed the greatest growth response to thinning. From the data presented in this study, a final density in the range of 200-300 trees/ha is recommended. This density would improve the growth of individual trees during a rotation length of 20 to 25 years without seriously under utilizing site resources.
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 | 0.000 | 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".