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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".