Properties of Veneer and Veneer-Based Products from Genetically Improved White Spruce Plantations
Bibliographic record
Abstract
This study examined the suitability of genetically improved fast-growing and short-rotation plantations for veneer-based products. The materials came from a 36-year-old white spruce (Picea glauca) half-sib progeny/provenance trial located in two regions (sites) of Quebec. A total of 270 sample trees were collected for the study, 130 trees from St-Ignace in the Gaspé Region and 140 trees from Valcartier near Quebec City. Veneer from the Valcartier site had a mean wood density of 0.353 g/cm3 and a mean modulus of elasticity (MOE) of 9.48 GPa (1.375 million psi). Veneer from the St-Ignace site had a mean wood density of 0.345 g/cm3] and a mean MOE of 8.05 GPa (1.167 million psi). The differences in veneer wood density and MOE between the two sites were statistically significant. Compared to other Canadian species commonly used for veneer products, the genetically improved fast-growing and short-rotation white spruce yielded considerably lower veneer stiffness.The plantation-grown white spruce veneer from both sites was knotty. Ninety-eight percent of the veneer was classified as visual grade C. The visually graded veneer would be suitable for sheathing grade plywood. With proper stress grading, 14% of the white spruce veneer was suitable for 12.41 GPa (1.8 million psi) grade laminated veneer lumber (LVL), and another 24% of the veneer was suitable for a lower 10.34 GPa (1.5 million psi) grade of LVL, or as core plies for LVL manufacture. The remaining 62% of the stress-graded veneer was suitable for sheathing grade plywood.
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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 | 0.000 |
| 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 | 0.001 | 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".