Variability in oil content and fatty acid composition of Ethiopian and introduced cultivars of linseed
Bibliographic record
Abstract
Abstract This study was undertaken to assess the variability in oil content, oil yield and fatty acid composition of 60 linseed cultivars and to identify suitable accessions for use in future breeding and development endeavours in Ethiopia. Mean oil contents ranged from 291 to 359 g kg−1, while oil yields varied between 1443 and 3276 g m−2. Exotic introductions, especially those from Canada such as CDC‐VG, had higher oil contents than the local cultivars. Thus the introduction of exotic materials should be given more emphasis through germplasm exchange programmes. Unsaturated fatty acids were the major components in the oils, varying significantly (P < 0.01) from 859 to 906 g kg−1, while minor saturated fatty acids were present at 84–119 g kg−1. The contents of oleic, linoleic and linolenic acids were 148–293, 109–161 and 470–591 g kg−1 respectively. Although accessions with variable linolenic acid contents were identified, this variability was insufficient to develop genotypes with less than 20 g kg−1 linolenic acid for cooking oil through conventional crossing and selection methods. Hence mutation techniques and the introduction of exotic lines should be regarded as alternative approaches to obtain linseed genotypes with low linolenic acid contents. Copyright © 2004 Society of Chemical Industry
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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) | 0.000 | 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".