Simple sequence repeat analysis of new potato varieties developed in Alberta, Canada
Bibliographic record
Abstract
The worldwide demand for potato production requires the constant development of new potato varieties with improved yield, quality, disease resistance, and abiotic tolerance. However, cultivar registration is preceded by a long process to morphologically and physiologically characterize the plants. Notably, this process can be expedited by DNA marker analysis. Simple sequence repeats (SSRs), also known as microsatellites, are the most common reliable DNA markers used to discriminate between genotypes. In this study, 20 potato varieties, including five new genotypes developed in Alberta, Canada, were fingerprinted using 10 SSR markers selected for their high discriminatory power. Different SSRs were amplified from potato DNA using specific primers, and the DNA fragment sizes were analyzed by denaturing polyacrylamide gel electrophoresis. The number of alleles per locus ranged from two for the SSR marker STPoAc58 to six for STM0030 and STM0037 with an average of 4.4. In addition, a total of 77 unique patterns were observed for the 10 SSR markers. The polymorphic information content ranged from 0.477 to 0.802 with an average of 0.675 per locus. In this study, STM0037, STM1016, and STM1104 were found to be the best SSR markers to detect genetic differences between potato varieties. A minimum of two markers was required to distinguish between all 20 genotypes. Most importantly, this highly informative molecular tool confirmed that the developed potato varieties were genetically different from their respective maternal lines and potentially constituted new cultivars.
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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 | 0.000 | 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".