Thinning Intensity and Pruning Impacts on<i>Eucalyptus</i>Plantations in Brazil
Bibliographic record
Abstract
A thinning intensity experiment using theEucalyptus grandis×E. urophyllahybrid was conducted at three sites in Bahia State, Brazil. The treatments were a combination of thinning intensities and pruning: 20%, 35%, and 50% basal area removed with no pruning and 35% basal area removed plus pruning (at 27 months). Plots were measured roughly annually from 27 to 165 months. Thinning was implemented on all sites at 58 months and again at 142 months at two of the sites. One of the sites was harvested at 87 months of age. A linear mixed model was applied separately to each installation to test for differences among treatments for mean increment of height, dominant height, quadratic mean diameter, and volume outside bark at the plot level. Additionally, differences in mean monthly increment of basal area and volume outside bark as a percentage of the value at the beginning of the increment period were examined. Increased thinning intensity increased all tree-level variables except dominant height. Pruning had no impact. Observed mortality on all plots was quite low. Thinning intensity response varied among sites and with time since thinning; however, the thinning intensity response was consistent through time among the installations.
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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 | 0.001 |
| 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) | 0.000 | 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".