Singular-Value Partitioning in Biplot Analysis of Multienvironment Trial Data
Bibliographic record
Abstract
Multienvironment trials (MET) are conducted every year for all major crops throughout the world, and best use of the information contained in MET data for cultivar evaluation and recommendation has been an important issue in plant breeding and agricultural research. A genotype main effect plus genotype × environment interaction (GGE) biplot based on MET data allows visualizing (i) the which-won-where pattern of the MET, (ii) the interrelationship among test environments, and (iii) the ranking of genotypes based on both mean performance and stability. Correct visualization of these aspects, however, requires appropriate singular-value (SV) partitioning between the genotype and environment eigenvectors. This paper compares four SV scaling methods. Genotype-focused scaling partitions the entire SV to the genotype eigenvectors; environment-focused scaling partitions the entire SV to the environment eigenvectors; symmetrical scaling splits the SV symmetrically between the genotype and the environment eigenvectors; and equal-space scaling splits the SV such that genotype markers and environment markers take equal biplot space. It is recommended that the genotype-focused scaling be used in visualizing the interrelationship and comparison among genotypes and the environment-focused scaling be used in visualizing the interrelationship and comparison among environments. All scaling methods are equally valid in visualizing the which-won-where pattern of the MET data, but the symmetric scaling is preferred because it has all properties intermediate between the genotype- and the environment-focused scaling methods.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".