Stiffness of Douglas-fir lumber: effects of wood properties and genetics
Bibliographic record
Abstract
Because stiffness (modulus of elasticity (MOE)) is important for structural wood products, breeders and silviculturists seek to efficiently measure and improve this trait. We studied MOE in a 25-year-old progeny test of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) using field-based tools (ST300 and HM200) to measure stress wave MOE of standing trees and logs. We measured density, static bending MOE, and transverse vibration MOE on 2 × 4s, and density, SilviScan MOE, and SilviScan microfibril angle on small clearwood samples. Bending MOE had moderate to strong phenotypic and genetic correlations with stress wave MOE of trees and logs, transverse vibration MOE of 2 × 4s, and the densities of 2 × 4s and basal wood discs but was weakly correlated with the numbers and sizes of knots. The best lumber grade had the highest bending stiffness and smallest edge knots. Bending stiffness had a strong positive correlation with the density of small clearwood samples and a moderate negative correlation with microfibril angle. Compared with microfibril angle and edge knots, path analyses indicated that density had the strongest direct effect on bending MOE. We recommend that breeders measure and select for stress wave velocity to improve bending stiffness in Douglas-fir. Genetic gains can be increased by including wood density, but genetic selection for fewer or smaller knots will be ineffective.
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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 | 0.001 | 0.001 |
| 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".