Development of microsatellite markers in potato and their use in phylogenetic and fingerprinting analyses
Bibliographic record
Abstract
Three genomic libraries were constructed using a mixture of DNA from Solanum phureja Juz. & Buk., and S. chacoense Bitt. Two of the libraries were enriched for ATT and GT repeats (a 27-fold enrichment was achieved). In total, 3500 clones of the conventional library, 1,000 of the library enriched for ATT, and 12,000 of the one enriched for GT were screened with five different repeat motifs, and a total of 18 primer pairs was obtained. Another group of 12 primer pairs was obtained from the SSR-containing sequences in the public databases (18 SSR-containing sequences were utilized). From among 30 newly developed primer pairs, 12 previously published ones, and 12 pairs developed for tomato, 7 were used to identify 12 different potato cultivars and introductions, and 12 were used to study phylogenetic distance among seven wild and cultivated potato species. Two SSR markers were sufficient to discriminate the 12 cultivars. The mean number of alleles per polymorphic locus was 5 for the 12 cultivars and 4.5 for the seven species. The results obtained in this study confirm those achieved in similar studies in other plant species regarding the abundance and use of SSR markers in identifying species and cultivars.
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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 | 0.001 | 0.002 |
| 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".