Genetic correlations between spiral grain and growth and quality traits in Picea abies
Bibliographic record
Abstract
In Norway spruce ( Picea abies (L.) Karst), spiral grain is a major cause of twist development in sawn timber; this problem could be addressed by breeding for reduced grain angles. This study presents estimates of genetic correlations between grain angle under bark and height and diameter growth; branch number, angle, and thickness; stem straightness; ramicorn occurrence; and pilodyn penetration using data from three progeny trials. The genetic relationship between grain angle development and radial growth was also investigated by measuring multiple annual rings (3–15) in stem sections from two clonal trials. Grain angles under the bark exhibited substantial heritability but near-zero genetic correlations with all the other traits studied. The genetic correlations between multiple ring grain angle and radial growth were also close to zero among all rings. However, radial growth exhibited positive genetic correlations with grain angles at specific distances from the pith and with radial grain angle trends, suggesting that the higher grain angles in juvenile wood extend further from the pith as a result of increased radial growth. Therefore, from a sawtimber perspective, the genetic relationship with radial growth may be unfavourable, despite the lack of genetic correlations between grain angle and radial growth at any particular annual ring.
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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".