Best linear unbiased prediction of clonal breeding values and genetic values from full-sib mating designs
Bibliographic record
Abstract
Full-sib progeny tests with clonal replicates may provide better breeding value estimates and the greatest genetic gain in a tree improvement program. Clonal breeding values (CBV) that combine the family and within-family breeding values due to additive genetic effects can maximize the genetic gain for advanced generation breeding. Clonal genetic values (CGV) that further incorporate full-sib family specific combining ability due to nonadditive genetic effect can maximize gain for a deployment program with clonal propagation techniques. The best linear unbiased prediction (BLUP) is the best statistical method for estimating both CBV and CGV because of its desirable statistical properties compared with the heritability-based gain calculation. A BLUP method for determining both the CBV and CGV for full-sib clonal progeny tests was proposed in this paper. The formulas for CBV and CGV were derived using general BLUP methodology, and formulas were derived for the calculations of their standard errors. An analytical method by using a standard statistical package (SAS PROC MIXED) was presented for CBV and CGV calculations from any full-sib mating designs.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".