Effects of Spacing on Early Growth Rate and Yield of Hybrid Eucalyptus Stands
Bibliographic record
Abstract
Optimizing tree spacing in a forest plantation is one of the main management techniques to improve stand quality and productivity. Its influence on growth from an early age is an important matter for forest management. This study aims to evaluate the effect of tree spacing on early growth rate and yield over time in Eucalyptus grandis × Eucalyptus camaldulensis hybrids. The data were obtained from an experiment in Itamarandiba, Minas Gerais, Brazil. The plots were composed of five planting spacing (3.00 m × 0.50 m, 3.00 m × 1.00 m, 3.00 m × 1.50 m, 3.00 m × 2.00 m, and 3.00 m × 3.00 m) measured at the ages of 7, 12, 24, 36, 48, 61, 77, 85, and 102 months. Growth and yield were analyzed by fitting the Gompertz model and a baseline exponential model up to 36 months of age to evaluate the influence of early growth on the harvest age. A Pearson correlation matrix was also generated to find out the relationship between the mean annual increment in the respective treatments during the studied period. It was observed that a positive correlation in the average annual increase in the 3 × 2 and 3 × 3 spacings. It was verified that tree spacing influenced the yielded wood volume and the optimal harvest age. The early growth rate influences the optimal harvest, which may explain a possible loss of yield during the productive cycle of the forest stand.
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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".