A heritability-adjusted GGE biplot for test environment evaluation
Bibliographic record
Abstract
Test environment evaluation has become an increasingly important issue in plant breeding. In the context of indirect selection, a test environment can be characterized by two parameters: the heritability in the test environment and its genetic correlation with the target environment. In the context of GGE biplot analysis, a test environment is similarly characterized by two parameters: its discrimination power and its similarity with other environments. This paper investigates the relationships between GGE biplots based on different data scaling methods and the theory of indirect selection, and introduces a heritability-adjusted (HA) GGE biplot. We demonstrate that the vector length of an environment in the HA-GGE biplot approximates the square root heritability ( $$ \sqrt H $$ ) within the environment and that the cosine of the angle between the vectors of two environments approximates the genetic correlation (r) between them. Moreover, projections of vectors of test environments onto that of a target environment approximate values of $$ r\sqrt H $$ , which are proportional to the predicted genetic gain expected in the target environment from indirect selection in the test environments at a constant selection intensity. Thus, the HA-GGE biplot graphically displays the relative utility of environments in terms of selection response. Therefore, the HA-GGE biplot is the preferred GGE biplot for test environment evaluation. It is also the appropriate GGE biplot for genotype evaluation because it weights information from the different environments proportional to their within-environment square root heritability. Approximation of the HA-GGE biplot by other types of GGE biplots was discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".