Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution
Bibliographic record
Abstract
ABSTRACT Biplot analysis has been used for studying genotype × environment interaction (GE) or any two‐way table. Its descriptive and visualization capabilities along with the availability of user‐friendly software have enabled plant scientists to examine any two‐way data by a click on a computer button. Despite widespread use, the validity and limitations of biplot analysis have not been completely examined. Here we identify and briefly discuss six key issues surrounding overutilization or abuse of biplot analysis. We question (i) whether the retention of the first two multiplicative terms in the biplot analyses is adequate; (ii) whether the biplot can be more than a simple descriptive technique; (iii) how realistic a “which‐won‐where” pattern is identified from a biplot; (iv) what if genotypes and/or environments are random effects; (v) how relevant biplot analysis is to the understanding of the nature and causes of interaction; and (vi) how much the biplot analysis can contribute to detection of crossover interaction. We stress the need for use of confidence regions for individual genotype and environment scores in biplots to make critical decisions on genotype selection or cultivar recommendation based on a statistical test. We conclude that the biplot analysis is simply a visually descriptive statistical tool and researchers should proceed with caution if using biplot analysis beyond this simple function.
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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 | 0.061 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".