Use of GGE biplot methodology for genetic analysis of yield and related traits in melon (<i>Cucumis melo</i>L.)
Bibliographic record
Abstract
Dehghani, H., Feyzian, E., Jalali, M., Rezai, A. and Dane, F. 2012. Use of GGE biplot methodology for genetic analysis of yield and related traits in melon ( Cucumis melo L.). Can. J. Plant Sci. 92: 77–85. A complete diallel cross experiment of six local Iranian melon populations (Eyvanaki, Abasali, Tashkandi, Hose-sorkh, Mashhadi and Mirpanji) and one cultivar (Ananasi) was conducted. Fruit number, average weight per fruit, yield and acceptable yield were re-evaluated using GGE biplot methodology. The two principal components of biplot explained 70, 58, 86 and 88% of total observed variation for yield, acceptable yield, average weight per fruit and fruit number per plant, respectively. Mirpanji had the highest GCA for yield, acceptable yield and average weight per fruit, but the highest negative GCA for fruit number per plant. Abasali showed the highest positive GCA for fruit number. Biplot analysis allowed a rapid and effective overview of general combining ability (GCA) and specific combining ability (SCA) effects of the populations, their performance in crosses, as well as grouping patterns of similar genotypes.
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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 | 0.001 |
| 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 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".