Mega‐environment Analysis and Test Location Evaluation Based on Unbalanced Multiyear Data
Bibliographic record
Abstract
ABSTRACT Mega‐environment analysis and test location evaluation are two important issues for effective crop variety evaluation through multilocation variety trials. These must be done based on multiyear multilocation variety‐trial data, which are usually highly unbalanced. This paper presents a new graphical approach for conducting mega‐environment analysis and test location evaluation utilizing unbalanced multiyear variety trial data. It consists of three steps: (i) generating a G (genotypic main effect) plus GE (genotype × environment interaction), or GGE, biplot using a missing‐value estimation procedure and treating each location–year combination (trial) as an environment; (ii) summarizing the interrelations among test locations (L) in a GGL + GGE biplot, which is the same GGE biplot imposed with the test locations. The placement of a test location in the biplot is defined by the coordinates of all environments at the location; and (iii) summarizing any subregion (S) (i.e., mega‐environment) differentiation revealed in Step 2 in a GGS biplot, which is the same GGE biplot imposed with the subregions. The placement of a subregion in the biplot is defined by the coordinates of all environments in the subregion. The same GGL + GGE biplot can also be used to visualize the ability and stability of each test location to represent a target mega‐environment. Yield data from the 2006–2012 Quebec oat ( Avena sativa L.) registration and recommendation trials were analyzed as a demonstration.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".