Identification of molecular markers associated with oleic and linolenic acid in spring oilseed rape (<i>Brassica napus</i>)
Bibliographic record
Abstract
Abstract The quality of the oil derived from oilseed rape is determined by its fatty acid composition. Breeding oilseed rape for enhanced oil quality includes the development of cultivars with high oleic and low linolenic acid. Random amplified polymorphic DNA (RAPD) and intersimple sequence repeat (ISSR) techniques were investigated for the development of molecular markers for genes controlling oleic and/or linolenic acid. Markers that were identified were converted to sequence characterized amplified region (SCAR) markers for use in breeding. Molecular markers associated with these two fatty acids were identified in a doubled haploid population derived from a cross between the oilseed rape lines TO99‐5318‐20, very high oleic (>79%) and very low linolenic acid (<2%) × DH12075, high oleic (68%) and higher linolenic acid (>7%). Eight RAPD markers were associated with oleic and linolenic acid contents. The RAPD marker UBC 2830 accounted for 43% and 13% of the genetic variation for oleic and linolenic acid levels, respectively. The RAPD marker UBC 153550 accounted for 19% of the genetic variation for linolenic acid. The UBC 2830 fragment was converted to a SCAR marker. The markers identified in this study should be useful tools for the early generation selection of high oleic and low linolenic acid genotypes in oilseed rape breeding programmes.
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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 | 0.000 |
| 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".