Wood density variations of <i>E. urophylla</i> clone among growth sites are related to climate
Bibliographic record
Abstract
The Eucalyptus genus is extensively planted in Brazil for industrial use, and with the expansion of forest frontiers and climate changes, studies are needed on how these changes affect wood density (WD). The aim of this study was to evaluate the effect of some climate variables on WD of an Eucalyptus urophylla clone for a 4–5 year period. WD from trees collected in 12 sites were evaluated. Five growth rings were identified using a magnifying glass, X-ray densitometry, and forest inventory. WD was correlated with temperature ( T), precipitation ( P), soil water deficit, ring width, and current annual increment. There was no variation in WD in the first 20 months among sites. From that age on, WD was mainly correlated with T ( r > 0.6). The 1 °C increase in T resulted in a WD reduction of 0.014 g cm−1, and increases of 10 m³ ha−1 in the final volume were related to an increase of 0.004 g cm−1 in WD. Researchers and managers should continue to put efforts into broad experimental networks to assess the effects of climate change on the adaptation and wood quality of Eucalyptus clones. Highlights The influence of climate on E. urophylla wood density was greater after the third year of growth. The mean air temperature was the best correlated climatic variable with E. urophylla wood density. The cross-site climatic gradient was more important in wood density variation of E. urophylla than the variability among the years.
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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 | 0.001 | 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".