Assessment of genomic diversity among wheat genotypes as determined by simple sequence repeats
Bibliographic record
Abstract
Simple sequence repeats (SSRs) have been used to examine the genomic diversity of wheat (Triticum aestivum L.) germplasm. Thirteen wheat genotypes of diverse origin were analyzed with 43 selected SSRs to provide uniform and maximum genome coverage. A total of 156 allelic variants were detected at 43 SSR loci, ranging from two to eight per locus with an average of 3.6. The polymorphic information content (PIC) values of the loci ranged from 0.10 (Xgwm264) to 0.89 (Xgwm471 and Xgwm577). Genetic similarities calculated from SSR data ranged from 30.1 ('Era' and 'Klasic') to 90.1 ('Neepawa' and 'Thatcher') between genotypes. UPGMA analysis based on genetic distance estimates produced three loose groupings that were generally consistent with available pedigree information. Cultivars 'Neepawa' and 'Thatcher' are closely related. Their genetic relationship was confirmed by the facts that they share a common ancestor and are clustered together. There were two different 'Era' genotypes, one used in the 'Otane' pedigree and one used in this study. None of the other genotypes had a close common ancestor indicating any close genetic relationships. Principal coordinate analysis also confirmed this pattern of genetic diversity. A wide range of genomic diversity was observed among all the genotypes, proving them to be prime candidates for selective breeding for specific traits and broadening the genetic base.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".