Breeding for improved growth and juvenile corewood stiffness in slash pine
Bibliographic record
Abstract
The importance of integrating measures of juvenile corewood mechanical properties, modulus of elasticity in particular, with growth and disease resistance in tree improvement programs has increased. We investigated the utility of in-tree velocity stiffness measurements to estimate the genetic control of corewood stiffness and to select for trees with superior growth and stiffness in a progeny trial of 139 families of slash pine, Pinus elliottii Engelm. grown on six sites. Narrow-sense heritability estimates across all six sites for in-tree acoustic velocity stiffness at 8 years (0.42) were higher than observed for height (0.36) and diameter at breast height (DBH) (0.28) at 5 years. The overall type B genetic correlation across sites for velocity stiffness was 0.68, comparable to those found for DBH and volume growth, indicating that family rankings were moderately repeatable across all sites for these traits. No significant genetic correlations were observed between velocity stiffness, DBH, and volume growth. In contrast, a significant, but small, favorable genetic correlation was found between height and velocity stiffness. Twenty percent of the families had positive breeding values for both velocity stiffness and growth. The low cost, high heritability and nearly independent segregation of the genes involved with in-tree velocity stiffness and growth traits indicate that acoustic methods can be integrated into tree improvement programs to breed for improved corewood stiffness along with growth in slash pine.
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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 | 0.000 | 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".