DNA Fingerprinting of Closely Related Cultivars of Sweet Cherry
Bibliographic record
Abstract
Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers were evaluated in an effort to reliably DNA fingerprint sweet cherry ( Prunus avium L.) cultivars and advanced selections from the breeding program at the Summerland Research and Development Center (Summerland, BC, Canada). SSR markers were found that differentiated the 35 cultivars and selections tested. However, groups of cultivars closely related to the parental cultivars, Lapins and Sweetheart, were differentiated by only a few SSR markers each. These last few markers were discovered by specifically screening within these small groups of cultivars and the resulting markers had lower discriminating power ( D j ) statistics within the full set of 35 cultivars and selections. To further characterize the differences in one of these closely related groups, SNP markers were identified in the cultivar Sweetheart and an analysis was made of how these markers segregated into three of its open-pollinated progeny. Large blocks of the ‘Sweetheart’ genome (34%) did not contain informative SNP markers, which was consistent with its ancestry where the cultivar Van is both a parent and grandparent. The three progeny cultivars differed from ‘Sweetheart’ at 14%, 31%, and 29% of the 3011 SNP positions tested. These were located in blocks of linked haplotypes covering from 2.5 to 20 million bps each and were distinct for the three cultivars. The cultivar Staccato ® , which required the most effort for SSR marker discrimination, also had the lowest number of SNP position differences from ‘Sweetheart’ (14%). These informative SNP markers were located in only five small regions of the sweet cherry genome, which also contained the discriminating SSR markers and provides an explanation for the difficulty of locating SSR markers for this cultivar. In addition to clearly differentiating these cultivars, this SNP analysis shows the level of variation expected within this closely related group.
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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) | 0.000 | 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 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".