Effect of growth rate on wood properties of genetically improved Sitka spruce
Bibliographic record
Abstract
This study examined the wood properties of 24-year-old Sitka spruce ( Picea sitchensis (Bong.) Carr.) progenies with highly contrasting growth rates. The progenies were established as part of a breeding programme to improve growth rate and stem form. Trees from three progenies were selected with a high growth rate relative to an unimproved control of directly imported material from the Queen Charlotte Islands (QCI), British Columbia, Canada. Trees from a further three progenies were selected that displayed a similar growth rate to the QCI control. At the time of sampling, the fastgrowing progenies had a mean volume gain over the QCI and slow-growing progenies of ~70 per cent. Trees from the fast-growing progenies were found to have significantly larger branches, less latewood, and more compression wood in comparison with the QCI control and the slow-growing progenies. On the other hand, trees from the fast-growing progenies had a smaller grain angle than the QCI control. While fast-growing progenies had lower wood density than the control, this was not significantly different. These findings suggest that, in general, the criteria used to select Sitka spruce trees in the forest as potential candidates for the breeding population would indeed lead to significant improvements of the growth performance and grain angle from improved planting stock. Breeders need to be aware, however, of possible negative influences of such selection criteria on other stem and wood properties known to influence wood strength.
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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".