The potential for the genetic improvement of sawn timber traits in<i>Picea abies</i>
Bibliographic record
Abstract
This study evaluated the potential for the selective genetic improvement of the structural quality traits important in sawn Norway spruce (Picea abies (L.) Karst.) timber based on early and nondestructively assessed field traits. From a 34-year-old Norway spruce trial situated in southern Sweden, 401 butt logs were sampled and sawn to produce two 50 × 100 mm boards that were dried to an 18% moisture content. Structural quality traits were assessed, and genetic parameters were estimated, including additive genetic variance, heritability, and their genetic correlations with field traits. Board twisting, density, and modulus of elasticity (MOE, stiffness) were found to have appreciable heritabilities (0.23–0.44). Board twist was found to have a strong genetic correlation with grain angle measured under bark in the field (0.93), and both board MOE and density exhibited strong genetic correlations with field-assessed pilodyn penetration (–0.75 and –0.91, respectively). Although these observations were made on a thinning material comprising mainly juvenile wood, they nonetheless suggest grain angle and pilodyn penetration to be promising candidates as selection criteria for Norway spruce breeding. Heritabilities of other sawn timber traits were lower and the genetic correlations between these traits and field traits were also lower, variable, and had large estimation errors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".