Variation of lumber properties in genetically improved full-sib families of Douglas-fir in British Columbia, Canada
Bibliographic record
Abstract
Tree breeding to increase forest productivity and resilience is an active area of research. Many studies have examined wood traits of interest to lumber manufacturing, such as wood density, knottiness and microfibril angle, due to the generally negative correlation between rate of growth and wood quality. Relatively little is known, however, about the variation in structural parameters of lumber, i.e. modulus of elasticity (MoE) and modulus of rupture (MoR), in genetically improved trees. In this study we evaluate physico-mechanical properties of lumber from 12 full-sib families in a first-generation Douglas-fir progeny trial. Trees were harvested at age 33 and milled into boards that were tested for MoE and MoR, with specific gravity (SG) and acoustic velocity (AV) also measured. Results indicate that families with lower growth tend to perform better for MoE and MoR, although there are certain families that exhibit higher growth and better MoE and MoR. Boards with less juvenile wood had higher MoE and MoR indicating the significance of board orientation due to sawing pattern; this suggests that radial variation of wood properties is an important factor for genetically improved families. Overall, AV was a better predictor than SG for both MoE and MoR, indicating the potential of using AV in future tree breeding, as well as for product segregation. Findings from this study provide evidence to further develop breeding programmes of Douglas-fir in order to optimize wood production, product quality and ultimately value recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".