Selection Response in Subdivided Target Regions
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it