Improved Statistical Inference for Graphical Description and Interpretation of Genotype × Environment Interaction
Bibliographic record
Abstract
ABSTRACT Nonparametric resampling bootstrapping approach to constructing confidence regions (CR) for genotypic and environmental principal component (PC) scores recently has been used to statistically assess the biplot analysis of genotype × environment interaction (GE). However, it is possible to generate “greater‐than‐expected” CR due to nonunique singular value decomposition (SVD) of two‐way GE data from bootstrap samples. The objective of this study is to improve the current bootstrapping procedure to correct for the “systematic bias” due to the nonuniqueness of SVD through the use of Procrustes rotation. The Procrustes rotation is to compare the genotypic and environmental PC scores from bootstrap samples and original (target) data, with the comparison being done by rotating and then stretching and/or shrinking the PC scores from bootstrap samples such that the sum of squared distances between the corresponding elements of bootstrap and target scores is minimized. The bootstrapping and Procrustes rotation are implemented in an R package, bbplot/R. The analysis of two data sets from wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) cultivar trials shows that the CR for rotated genotypic and environmental scores are up to 10 times smaller than the CR for the corresponding unrotated scores. The shrunk CR constructed using the rotated scores for the biplot analysis reveal more definite delineations of mega‐environments than the assessment based on mere visual inspection of biplots. Thus, the improved bootstrapping approach will construct the more precise CR for the genotypic and environmental PC scores, thereby facilitating the correct use of biplot analysis for critical decisions on genotype selection or mega‐environment delineation.
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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 | 0.023 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".