Differences in wood anatomy and chemistry of a <i>Eucalyptus urophylla</i> clone explained by site climate conditions
Bibliographic record
Abstract
Environmental conditions can change both the quantity and quality of wood formation. This study aimed to evaluate anatomical and chemical changes in the wood of a Eucalyptus urophylla S.T. Blake clone cultivated in four sites of wide climatic conditions in Brazil. Radial samples were used to evaluate xylem anatomy along the growth cycles. Samples with a quarter of a disk were used to perform chemical analyses of extractives, total lignin (LG), syringyl/guaiacyl (S/G), holocellulose, and elemental analysis of wood. The elements Na, K, Ca, Mg, P, Mn, Fe, Zn, Ni, Cu, Cr, Cd, F, and Cl were also quantified. Correlations using the mean values of the variables per site were higher than those using values per tree growth cycle (years). Mean annual air temperature showed the highest correlations with wood density (r = −0.89) and the anatomical characteristics (vessel area: r = −0.68; fiber wall thickness: r = −0.70; vessel frequency: r = 0.74; and fiber lumen diameter: r = 0.90). Only LG and S/G showed significant correlations with the meteorological variables, with drier sites presenting a higher S/G. The anatomical characteristics change with regionwide climate features, while wood chemical characteristics showed weaker relations with climatic variations.
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".