Genetic response to within-family selection using molecular markers in some radiata pine breeding schemes
Bibliographic record
Abstract
Marker-assisted selection (MAS) provides an opportunity to increase the efficiency of within-family selection in forest tree breeding. Within-family MAS involves selection decisions first made on conventional breeding values and quantitative trait loci (QTL) information used for within-family selection. In this study genetic response obtained by using MAS was compared with conventional methods for three options: "full-sib family forestry," "clonal forestry," and "forward selection for deployment." This comparison was undertaken using stochastic simulation for a locus that explained 10 or 20% of the genetic variance. In the full-sib family forestry scenario, markers were used to select genotypes (among juvenile individuals in a family) for vegetative propagation. Markers were used to preselect genotypes for clonal testing in clonal forestry option. In case of forward selection for deployment option, offspring that have favourable marker haplotype and a superior phenotype were selected from each family. The comparison between the MAS and the conventional strategy was evaluated in genetic terms based on comparison of the average genetic merit of the genotypes used for deployment in production plantations. The relative genetic gain (%) using MAS were found to be 48% and 23% higher compared with conventional strategy for full-sib family forestry and clonal forestry options, respectively. In case of forward selection for deployment option, MAS was generally found to be providing higher genetic gain only when the heritability is low.
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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".