Breeding for Improved Oil Quality in <i>Brassica</i> Oilseed Species
Bibliographic record
Abstract
Oil quality in vegetable oils is determined by both nutritional and functional aspects, which are, in turn, primarily determined by the fatty acid profile (i.e., fatty acid composition) of the oil. The naturally occurring fatty acid composition of Brassica oils has been extensively modified using conventional plant breeding and biotechnology-based techniques to create unique and improved quality vegetable oils. New Brassica cultivars displaying a wide range of edible oil qualities have been developed, and commercialized in recent years. Improved Brassica cultivars which produce an industrial oil have also been developed and commercialized in the last two decades. World vegetable oil markets are highly competitive, so the steady improvement in oil quality of the Brassica oilseeds is essential to maintain or increase market share, and/or to create new niche markets. The main challenges facing Brassica oilseed breeders are (1) to determine the desirable fatty acid profiles of the oil for each end-use market application to create improved oils and (2) to quickly develop improved Brassica cultivars which can produce the new fatty acid profile oils at competitive prices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".