Comparison Between Genetic and Environmental Influences on Lumber Bending Properties in Young White Spruce
Bibliographic record
Abstract
This study investigated variation in lumber bending properties of white spruce (Picea glauca [Moench] Voss) and its correlation with tree growth, wood density, and knot size and number. A total of 242 sample trees from 39 open-pollinated families harvested from 36-year-old provenance-progeny trials at two sites in Quebec, Canada through a thinning operation were processed. The results indicate that mechanical properties of lumber from young white spruce plantation-grown trees are low. It appears that low wood density, the occurrence of numerous knots, and a high proportion of juvenile wood are the main factors contributing to the low lumber stiffness and strength properties. The narrow-sense heritability for lumber stiffness was low to moderate, whereas that of strength was hardly different from zero. Thus environmental growing conditions highly influence white spruce wood mechanical properties. The results also revealed a strong negative correlation between stem volume and lumber stiffness and strength at the family means, which suggests that selection for volume would have an indirect negative effect on lumber quality. However, the absence of such significant correlation at the phenotypic level also suggests that mass selection with vegetative propagation would be a promising avenue for improving white spruce wood properties without having to give up gains in volume.
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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) | 0.000 | 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".