Construction of Optimum Index to Maximize Overall Response Across Countries in the Presence of Genotype × Environment Interaction
Bibliographic record
Abstract
Sire effect is partitioned into two parts: constant effect unaffected by environments and interaction effect specific to each environment and responsible for genotype x environment (GE) interaction. Response to selection for constant effect is the same across environments, thus increasing genetic stability, whereas response to selection for interaction effect would vary depending upon environments. The conventional measure of GE interaction based on genetic correlation (gammaG) considers both constant and interaction components even though the constant component plays no role in GE interaction. In contrast, the proposed measure of GE interaction based on interaction correlation (gammaI) deals only with interaction component responsible for GE interaction and thus indicates the intensity of GE interaction generated by responsible genes. Constant and interaction effects with different economic weights were combined into optimum index to improve both genetic stability and overall response across countries (countries represent environments). Optimum index was more efficient than the unpartitioned index which was more efficient than selection in a single country except when economic weights between constant and interaction effects were equal. Optimum index and unpartitioned index were the same when these economic weights were equal. The advantage of optimum index over the other selection methods increases as the intensity of GE interaction increases. When the relative economic weights are equivalent among countries and between constant and interaction effects, selection in a country with a larger sire variance is more effective than selection in a country with a smaller sire variance.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".