SNP diversity within and among <i>Brassica rapa</i> accessions reveals no geographic differentiation
Bibliographic record
Abstract
Genetic diversity was studied in a collection of 61 accessions of Brassica rapa, which were mostly oil-type turnip rapes but also included two oil-type subsp. dichotoma and five subsp. trilocularis accessions, as well as three leaf-type subspecies (subsp. japonica, pekinensis, and chinensis) and five turnip cultivars (subsp. rapa). Two-hundred and nine SNP markers, which had been discovered by amplicon resequencing, were used to genotype 893 plants from the B. rapa collection using Illumina BeadXpress. There was great variation in the diversity indices between accessions. With STRUCTURE analysis, the plant collection could be divided into three groups that seemed to correspond to morphotype and flowering habit but not to geography. According to AMOVA analysis, 65% of the variation was due to variation within accessions, 25% among accessions, and 10% among groups. A smaller subset of the plant collection, 12 accessions, was also studied with 5727 GBS-SNPs. Diversity indices obtained with GBS-SNPs correlated well with those obtained with Illumina BeadXpress SNPs. The developed SNP markers have already been used and will be used in future plant breeding programs as well as in mapping and diversity studies.
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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 | 0.000 |
| 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".