Translocating seed sources to new geoclimatic environments has limited effect on lumber quality of eastern Canadian white spruce
Bibliographic record
Abstract
Assisted gene flow according to expected climate gradients is considered as a forest management strategy to mitigate impacts of environmental change on forest growth. However, the effects of seed translocation on wood properties and lumber quality remain unknown. This study evaluated the effect of provenance origin on lumber production and quality at rotation age in two white spruce provenance trials established in contrasting environments in eastern Canada. Based on 108 sample trees, which resulted in 943 pieces of lumber, average volume production per tree at the southernmost site was twice that of the production at the northern site. Provenance had a significant influence on growth and lumber strength in the first sawlog but had no effect on lumber stiffness and wood density. Although visual grade yields of No. 2 and better were high in both trials (over 86%), the machine stress rated (MSR) grade potential and percentage of lumber that met the bending stiffness design values of the visual grades were generally low (12%–26%). Hence, plantation-grown lumber should preferably be machine stress rated to ensure its fitness for structural applications in buildings. Management strategies aiming to efficiently sequester carbon should primarily maximize volume productivity in northern sites, as moving seed sources north still reduces provenance productivity, while breeding programs should aim to prevent decrease in lumber stiffness due to augmented productivity and shortened rotation cycles.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".