Genetic gains and levels of relatedness from best linear unbiased prediction selection of<i>Eucalyptus urophylla</i>for pulp production in southeastern China
Bibliographic record
Abstract
Breeding values for diameter at breast height (DBH), tree height (HT), relative bark thickness (BKR), and pilodyn penetration (PP) in Eucalyptus urophylla St. Blake plantations were predicted with best linear unbiased prediction (BLUP) approach. These values along with their economic weights derived from a previous study were then used to estimate economic genetic gains for three breeding objectives (pulp, woodchips, and wood volume) in southeastern China. The results showed substantial gain can be expected from selecting top 5% trees, with a reduction of up to US$35 for producing a tonne of ovendry pulp. However, actual gains can be strongly influenced by how the breeding objectives have been defined and whether the key traits have been included in the selection criteria. This study also showed that problem in the increase of coancestry associated with selection on BLUP would not be serious, with average coancestry amongst the selected population was less than 1%. More importantly, an unrestricted multiple-trait BLUP selection did not result in the same increase in relatedness in the selected population than it does for the single trait situation.
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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) | 0.000 | 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".