Genetic relationships between wood quality traits and diameter growth of juvenile core wood in Sitka spruce
Bibliographic record
Abstract
The trend towards shorter rotations in planted conifer stands has resulted in a reduction in the proportion of mature wood relative to juvenile core wood, raising concerns that the mechanical performance of sawn battens will be affected. The potential to improve the wood quality of the juvenile core of Sitka spruce (Picea sitchensis (Bong.) Carrière) without compromising growth rate was investigated. Rapid and inexpensive indirect methods of assessing wood properties on standing trees using a Pilodyn gun and acoustic velocity were compared with direct measurements made on wood samples cut from the juvenile core. Strong genetic correlations were observed between Pilodyn gun values and direct measures of density (–0.76) and between the square of acoustic velocity and modulus of elasticity (0.73). The genetic correlation between the square of acoustic velocity and microfibril angle was also strong (–0.84). These results suggest that indirect assessments of wood properties within juvenile core wood are sufficiently reliable for these techniques to be used in the Sitka spruce breeding programme. Although a strong negative genetic correlation between diameter at breast height and density was noted (–0.79), sufficient variation exists within the breeding population to select families with both good growth rate and high modulus of elasticity wood.
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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".