Balancing selection gain and genetic diversity in the genomic planning of crosses
Bibliographic record
Abstract
Abstract Creating new genetic variation by crossing two or more parents is the initial and often most important step when developing new crop varieties. Hence, several mate selection indices have been suggested to support the planning of crosses in genomic breeding pipelines that have been established in many breeding programmes in recent years. The corresponding index weights are however difficult to determine objectively, and these indices often feature weights determined by laborious grid searches or rules of thumb. The aim of this study was to compare methods that employ the latter approach with mate selection indices based on desired gains in simulations and an empirical Fusarium head blight experiment for winter wheat. The results indicated that the suggested desired gain indices outperform routinely used methods in terms of reaching a favourable balance between the short‐term selection gain, long‐term selection gain, as well as the genetic diversity. They might have beyond that a high prospect for making a broader spectrum of genetic diversity accessible in the framework of germplasm exchange between breeding programmes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".