Genetic improvement for pulpwood and peeled veneer in<i>Eucalyptus nitens</i>
Bibliographic record
Abstract
Genetic improvement of wood properties affecting the quality of pulpwood and peeled veneer products is of general interest to tree breeders worldwide. If the wood properties of Eucalyptus nitens (H. Deane & Maiden) Maiden are under genetic control and the correlations between them are favourable, it may be possible to breed to simultaneously improve the plantation resource for both products. Acoustic wave velocity (AWV) measured in standing trees can predict wood stiffness, basic density, and kraft pulp yield (KPY) and therefore has the potential for use in tree breeding programs. From an E. nitens progeny trial in Tasmania, 540 trees were selected for rotary peeling. Of the wood properties assessed, there were significant differences among races in diameter, stem straightness, standing-tree, log, and billet AWV, and near infrared predicted cellulose content (CC). All traits displayed significant within-race genetic variation, and genetic correlations between AWV and veneer sheet modulus of elasticity (MOE) and between AWV and KPY and CC were strongly positive and highly significant. A similar relationship was found between veneer sheet MOE and KPY and between diameter at breast height and veneer sheet MOE. Basic density was genetically correlated with AWV and veneer sheet MOE. Results indicate that it should be possible for breeders to simultaneously improve properties in pulpwood and peeled veneer products and that AWV measured in the standing tree shows promise as a breeding selection criterion for both pulpwood and peeled veneer products.
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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 | 0.000 | 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".