Mega‐environment Analysis and Test Location Evaluation Based on Unbalanced Multiyear Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it