Wood stiffness and strength as selection traits for sawn timber in Acacia auriculiformis
Bibliographic record
Abstract
Acacia auriculiformis A. Cunn. ex Benth. is an important planting tree species, but little attention has been paid to its wood properties, such as shrinkage, stiffness, strength, and basic density, which are important for use in structural and appearance-grade timber applications. Here we report the genetic variation in static bending stiffness and strength of wood in a 5½-year-old clone trial in southern Vietnam and the genotypic correlations among these traits and tree diameter, wood shrinkage, and basic density. There was significant variation in stiffness and strength among 40 randomly selected clones. Clonal repeatability (H2) was high for stiffness and moderate for strength. There was no consistent pattern of difference between heartwood and sapwood for the estimates of H2 for stiffness and strength, whereas the estimates of H2 were lower for heartwood density than for sapwood density. Diameter showed a significant negative genotypic correlation with stiffness but a nonsignificant correlation with strength. Genotypic correlations between density and stiffness, and density and strength, were positive, but only the latter was significant. Stiffness and strength were not significantly correlated with wood shrinkage. There is potential to simultaneously improve tree growth, wood basic density, and the mechanical properties of juvenile wood of this species.
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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) | 0.001 | 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".