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Record W2137640631 · doi:10.1002/jsfa.1698

Variability in oil content and fatty acid composition of Ethiopian and introduced cultivars of linseed

2004· article· en· W2137640631 on OpenAlexaboutno aff
Adugna Wakjira, Maryke Labuschagne, A. Hugo

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
FundersUniversiteit van die Vrystaat
KeywordsLinseed oilCultivarGermplasmLinolenic acidLinoleic acidComposition (language)Oleic acidFood scienceFatty acidYield (engineering)ChemistryHorticultureBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.118

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.208
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations60
Published2004
Admission routes1
Has abstractyes

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