Genetic variation in veneer quality and its correlation to growth in white spruce
Bibliographic record
Abstract
This study investigated the genetic variation in veneer quality of white spruce (Picea glauca (Moench) Voss) and its correlation with tree growth and wood density. A total of 270 sample trees from 35 families were harvested from 36-year-old provenance-progeny trials at two sites through a thinning operation. A 36-cm long bolt was collected from each sample tree at an 8-foot (or 2.45 m) height for this veneer quality study. The results indicate that conversion of fast-grown white spruce into veneer and plywood may present some problems. Low wood density, numerous knots, and possibly a high proportion of juvenile wood appear to be major factors contributing to low veneer stress grading, resulting in a production of low-quality veneer. This study also showed that the environmental factors at the two sites played an important role in determining veneer quality and tree radial growth. The narrow-sense heritability for veneer density, veneer modulus of elasticity, and veneer roughness were 0.62, 0.13, and 0.14, respectively. The results revealed considerable phenotypic variation and relatively high additive genetic variation in the veneer modulus of elasticity. The phenotypic and genetic correlations between ring width and veneer density or veneer modulus of elasticity were negative. A positive phenotypic and genetic correlation was found between veneer density and veneer modulus of elasticity. This suggests that selection for tree volume growth in white spruce would lead to a decrease in wood density and veneer stiffness.
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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".