Molecular characterization of potato cultivars using SSR markers
Bibliographic record
Abstract
The potato crop has a very narrow genetic base, so the use of molecular markers is a very important tool in the characterization of germplasm banks and evaluation of genetic divergence. The objective of this study was to identify, using microsatellite or simple sequence repeat (SSR) markers, 38 accessions of potato from two collections of commercial cultivars. For the molecular characterization 10 loci were used, generating a total of 46 alleles, which were analyzed as binary data. A cluster analysis was performed with the Jaccard´s similarity coefficient and the UPGMA method, using the software NTSYSpc. On average, the number of alleles per locus was 4.6, ranging from two alleles for primers STM1049, STM 1053 and STM 1104 to 12 alleles per locus for primer STM0019a. Of the 46 alleles, only five were monomorphic, therefore presenting 89.1% polymorphism. The polymorphism information content (PIC) varied from 0.13 to 0.86, with an average of 0.54. The Jaccard´s coefficient varied from 0.41 to 0.93, showing high genetic variability among accessions. Two possible duplicates [Atlantic (Canada) and Atlantic (Chile), and Colorado and Ágata (EPAMIG) (duplicates with these SSRs, which did not separate them)] were identified. High similarity was also shown by cultivars Chipie and Melodie (EPAMIG), Voyager and Gourmandine (EPAMIG), Eole and Caesar (EPAMIG), and Cupido and Santé (Pirassu). The most genetically divergent accessions (Lady Rosetta and HPC-7B) were also identified.
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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 | 0.001 |
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".