Defining Sunflower Selection Strategies for a Highly Heterogeneous Target Population of Environments
Bibliographic record
Abstract
Genotype × environment (G × E) interactions can be a major impediment to genetic progress in sunflower (Helianthus annuus L.) breeding for Argentina. Previous studies revealed that northern and central environments show repeatable differences in genotype discrimination, suggesting some G × E interactions could be accommodated by selecting for specific adaptation. In this study, a trial dataset of 10 hybrids grown over 46 environments was used to validate this megaenvironment definition, to determine the value of division of the sunflower region of Argentina, and to define optimal testing strategies to balance resources between subregions. Pattern analysis confirmed the northern and central megaenvironments. Subdivision of the target region and the testing resources increased the within‐subregion genotype to G × E interaction ratios and did not decrease trial repeatabilities. The genetic correlation between target region and its subregions was 0.36. In contrast to studies for barley in Canada, the calculated ratios of correlated response in a subregion to indirect selection in the undivided target region relative to direct response in the subregion demonstrate that division of the sunflower region is 3× more effective than selecting for broad adaptation to the undivided target region. The unpredictable G × E interactions within subregions should be accommodated by selecting for broad adaptation. In the northern subregion, there is scope to redefine testing strategies by replacing years with locations with no cost in performance predictability. Testing resources can be balanced based on the market value of the two subregions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".