Broadening Genetic Diversity in Canola Using the C‐Genome Species <i>Brassica oleracea</i> L.
Bibliographic record
Abstract
ABSTRACT Genetic diversity in spring type canola (Brassica napus L., AACC genome, 2n = 38) cultivars is narrow. Limited effort has been made to utilize genetic diversity from the diploid species Brassica oleracea L. (CC genome, 2n = 18) due to lack of canola quality traits in seeds of this species. The objectives of this study were to assess the feasibility of introgressing canola quality traits from B. napus into B. oleracea for the purpose of developing canola quality B. oleracea as well as development of B. napus with greater genetic diversity from B. oleracea while retaining canola quality traits. Two inbred (B. napus × B. oleracea) × B. oleracea populations were generated using B. napus ‘Hi‐Q’ and A01‐104NA and B. oleracea var. alboglabra (L. H. Bailey) Musil (Chinese kale). These populations were assessed for seed quality, effectiveness of selection based on morphological traits, genetic diversity using simple sequence repeat (SSR) markers, and ploidy levels using flow cytometry and cytological analysis of meiotic chromosomes. Zero‐erucic and low glucosinolate types were recovered from a relatively small segregating population. Morphological grouping could not reliably be used to select B. oleracea plants with 2n = 18; all BC1S6 families had nuclear DNA content similar to the B. napus parents. Marker analysis revealed a high level of B. oleracea alleles among backcross lines. These findings suggest that introgression of genetic diversity from the diploid B. oleracea C‐genome into stable, canola quality B. napus type lines is feasible and may have great potential in developing genetically diverse spring type varieties.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".