Genetic variation in wood properties of interior spruce. I. Growth, latewood percentage, and wood density
Bibliographic record
Abstract
Quantitative genetic variation in growth, latewood percentage, and wood density was investigated for British Columbia's interior spruce (the common name for white spruce, Picea glauca (Moench) Voss; Engelmann spruce, Picea engelmanni Parry ex Engelm.; and their hybrids). The study included 160 half-sib families from the East Kootenay and Prince George regions. At the time of sampling, progeny tests for those two regions were 20 and 22 years old, respectively. Univariate and multivariate restricted maximum likelihood (REML) estimates of genetic parameters were obtained. Estimates of genetic variances and heritabilities differed greatly across planting sites for the examined traits, especially after transplantation between the regions. Significant negative genetic correlation between overall growth and wood density was found for the East Kootenay progenies, while negative but nonsignificant genetic correlation between these traits was found for the Prince George progenies. Generally, there was no significant decrease in heritability for ring width and latewood percentage in successive growth rings. A general age trend for genetic correlation between those traits was not apparent, except that the correlation remained negative during the observed period. Our results show that it is not possible to select certain families as superior based on 1-year results because of the family by growing season interactions. Nevertheless, genetic ageage correlations for cumulative increments were high, having a decreasing trend with increasing difference in age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".