White spruce wood quality for lumber products: priority traits and their enhancement through tree improvement
Bibliographic record
Abstract
Abstract White spruce breeding has traditionally focused on genetic improvement for high productivity, targeting mostly stem volume. However, new challenges, such as the effects of climate change on individual trees and ecosystems and the changing nature of forest products markets, require action to address these issues while providing improved stock for seed and seedling production. This work aims to define the relative importance of white spruce wood traits for the lumber industry, with the intent of guiding tree breeding programs. This manuscript examines: (1) wood features that are important for lumber, (2) the genetics of wood traits and their improvement and (3) the level of acceptance of raw material with optimal traits, as obtained from a survey conducted with sawmills. Stem diameter at stump height, wood density, bending strength and stiffness were determined as the most important properties for sawmills. Decay proportion, knot diameter, slope of grain and the proportion of compression wood were also determined as important for sawmills, but to a lesser extent. Breeding efforts aiming to produce lumber should focus on stems with improved growth that are capable of sustaining the production of dense wood in greater proportion.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".