Growth and wood properties of genetically improved loblolly pine: propagation type comparison and genetic parameters
Bibliographic record
Abstract
The use of clonal varieties in forestry offers great potential to improve growth traits (quantity) and wood properties (quality) of loblolly pine (Pinus taeda L.). Loblolly pine trees established via somatic embryogenesis (clones), full-sib zygotic crosses, and half-sib zygotic open-pollinated families were sampled to identify variation in growth and wood properties among and within clonal lines and zygotic controls. Increment cores 5 mm in diameter were collected at age 4 from a total of 2615 trees. Growth properties (diameter at 1.4 m and total tree height) and wood properties (whole-core density, latewood and earlywood density, and latewood percent) were measured for each tree sampled in the study. Overall, growth properties were better for full-sib seedling than for clonal lines, whereas wood density was higher for clonal lines than full-sib and open-pollinated seedlings. However, there were clonal lines with better growth and higher wood density. Clonal repeatability of both growth and wood properties across sampled sites and genetic correlations between growth and wood traits were determined, with higher repeatability observed for wood traits compared with growth traits. Significant genetic correlations were observed for tree height and wood properties, whereas weak correlations were observed for diameter and wood properties.
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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 | 0.000 |
| 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".