Genome-wide identification of SSR markers in the <i>Brassica</i> A genome and their utility in breeding
Bibliographic record
Abstract
Simple sequence repeat (SSR) markers can be applied to genotyping projects at low cost with inexpensive equipment. The objective of this study was to develop SSR markers from the publically-available genome sequence of Brassica rapa and provide the physical position of these markers on the chromosomes for use in breeding and research. To assess the utility of these new markers, a subset of 60 markers were used to genotype 43 accessions of B. rapa. Fifty-five markers from the 10 chromosome scaffolds produced a total of 730 amplicons, which were then used to perform a phylogenetic analysis of the accessions, illustrating their utility in distinguishing between a wide range of germplasm. In agreement with similar studies of genetic diversity, our markers separated accessions into distinct genetic pools including Chinese cabbage, Chinese winter oilseed, European winter oilseed, Canadian spring oilseed, pak-choi, turnip, and yellow sarson. The results further illustrate the presence of a high level of genetic diversity in B. rapa, and demonstrate the potential of these SSR markers for use in breeding and research.
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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".