Selection Response in Subdivided Target Regions
Bibliographic record
Abstract
In a small target region, it may be possible to exploit local adaptation to increase gains from selection. However, in a large region more extensive testing is usually possible, resulting in more precise estimation of genotype means. A correlated response model was adapted to determine if division of a large target region is likely to increase gains. Genotypic value in a large region and constituent subregions are considered correlated traits. Correlated response in a subregion to indirect selection across the undivided region, relative to direct response to selection within the subregion, is expressed as a function of heritability in the undivided region ( H ) and in the subregion ( H i ), and of the genotypic correlation between region and subregion means ( r G′ ). r G′ depends on the magnitude of the genotype × subregion interaction (σ 2 GS ) relative to the genotypic variance (σ 2 G ). σ 2 GS is the portion of the genotype × location interaction (σ 2 GL ) caused by local adaptation, rather than by random site‐to‐site variability in genotype means. Subdivision can increase heritability through the addition of σ 2 GS to the numerator of H i , but this may be offset by reduced replication across locations within the subregion. Modeling using variance estimates from several cereal programs indicated that, unless σ 2 GL is large relative to σ 2 G and at least 30% of σ 2 GL is due to σ 2 GS , subdivision is unlikely to increase response. These results help explain the success of breeding programs that test broadly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".