Genetic analysis of <i>Eucalyptus globulus</i> diameter, straightness, branch size, and forking in Western Australia
Bibliographic record
Abstract
Eucalyptus globulus Labill. is increasingly considered for supply of solid-wood products such as sawlogs, yet genetic studies of solid-wood traits have been lacking. We estimated genetic parameters of growth and form traits that affect log value in full-sib families from two advanced-generation breeding populations on eight sites in Western Australia. Mean single-site heritability was 0.11 ± 0.01 for diameter at breast height (DBH), 0.28 ± 0.05 for stem straightness, 0.09 ± 0.02 for branch thickness, and 0.05 ± 0.02 for forking incidence. Dominance effects were significant (p < 0.05) at four sites for DBH and branch thickness and at three sites (one population) for straightness. Mean intersite additive genetic correlations were 0.76 ± 0.06 for DBH (n = 7), 0.75 ± 0.11 for stem straightness (n = 7), and 0.58 ± 0.07 for branch thickness (n = 4). Mean intersite dominance genetic correlations were 0.90 ± 0.04 for DBH (n = 7), 0.26 ± 0.27 (n = 4) for straightness, and 0.68 ± 0.11 for branch thickness (n = 3). Additive genetic correlations between DBH and straightness ranged from –0.71 ± 0.23 to 0.33 ± 0.19 with an average of –0.18 ± 0.12 (n = 8). Genetic correlations between DBH and branch thickness were mostly weak although straightness was generally associated with thinner branches (mean additive correlation 0.44 ± 0.15, n = 6). We conclude that prospects appear favourable for improving the solid-wood value of E. globulus by selection and breeding.
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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) | 0.000 | 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".