Multivariate association of wood basic density with site and plantation variables in <i>Eucalyptus</i> spp.
Bibliographic record
Abstract
Wood density, an important parameter for evaluating forest biomass productivity and wood product quality control, is influenced by a complex combination of variables of forest plantations, including environmental conditions and the management practices adopted. In this paper, we demonstrate that three site variables (annual rainfall, temperature, and soil texture) and 10 plantation variables (e.g., age and genetic material) are associated with basic wood density (evaluated in two situations: with and without bark) in 936 trees of different species of Eucalyptus L’Hér across five distinct edaphoclimatic regions in Brazil. A canonical correlation analysis was used to identify the most contributory variables affecting wood density. The variables globally associated with high basic wood densities were, in order of importance, the genetic material and area per tree (both under direct control of plantation managers), as well as mean annual temperature and soil texture of the site. These results confirmed the advantage of using clonal material (instead of seedling origin material) planted in larger spacings in sites with higher mean annual temperatures and clayey soils to obtain higher basic wood densities. Conversely, low basic wood densities were associated with high-productivity sites, higher rainfall, and plantations with a higher basal area per stem in second rotation.
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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 | 0.001 | 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".