Assessment of genotype × trait interaction of rye genotypes for some morphologic traits through GGE biplot methodology
Bibliographic record
Abstract
<p>Effective interpretation of the data on breeding programs is important at all stages of plant improvement and the genotype by trait (GT) biplot was used for two-way wheat dataset as genotypes with multiple traits. For this propose, 18 rye genotypes with specific characteristics were evaluated in randomized block design with four replications. The GT biplot for rye dataset explained 61% of the total variation of the standardized data (the first two principal components explained 40 and 21% respectively). The polygon view of GT presented for 11 different traits of rye cultivars showed six vertex cultivars as G1, G3, G6, G8, G11 and G13 whose genotype G8 had the highest values for most of the measured traits. Generally based on vector view, ideal genotype and ideal tester biplots, it was demonstrated that the selection of high seed yield will be performed via seed number per spike, first internode weight, number of spike per area and harvest index. These traits should be considered simultaneously as effective selection criteria evolving high yielding rye cultivars because of their large contribution to seed yield. The genotypes G8 and G7 following to genotypes G3, G18 and G19 could be considered for the developing of desirable progenies in the selection strategy of rye improvement programs.</p>
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".