Effects of genetics on the wood properties of Sitka spruce growing in the UK: bending strength and stiffness of structural timber
Bibliographic record
Abstract
Mechanical tests were conducted on structural timber from a 37-year-old Sitka spruce (Picea sitchensis (Bong.) Carr), progeny trial located in Kershope Forest, Cumbria, UK. Values of modulus of rupture (MOR) and global modulus of elasticity (MOEG) in bending and density were compared between timber cut from four of the eight different seed lots which made up the experiment. Three of these seed lots were open-pollinated progeny of selected plus trees, while the fourth consisted of trees grown from an unimproved collection imported from the Queen Charlotte Islands (QCI) in British Columbia, Canada. The progenies from the plus trees were selected for their contrasting growth rates, stem form and wood density relative to the QCI control. Overall, the timber had characteristic values for density, MOR and MOEG consistent with the requirements for the C16 strength class. A significant difference in timber basic density was observed between two of the seed lots; however, there was no difference in MOR or MOEG between any of the seed lots. Most of the variation in strength properties in the study was attributable to differences between individual trees (»40 per cent) and individual pieces of timber from within a tree (»50 per cent), with only a small amount (⪅5 per cent) due to treatment differences. Results indicate that gains in merchantable log volume that have been achieved due to tree breeding do not appear to have been offset by a reduction in the mechanical properties of timber.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".