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Molecular basis of the low linolenic acid trait in soybean EMS mutant line RG10

2009· article· en· W2046527242 on OpenAlexaff
Yarmilla Reinprecht, Shun-Yan Luk-Labey, Jamie Larsen, Vaino Poysa, Kangfu Yu, Istvan Rajcan, G. R. Ablett, K. Peter Pauls

Bibliographic record

VenuePlant Breeding · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsBiologyMutantLinolenic acidMethane sulfonateExonGeneGeneticsBiochemistryMutationLinumFatty acidMolecular biologyLinoleic acidBotany

Abstract

fetched live from OpenAlex

Abstract A possible solution to stability problems is to genetically reduce the content of linolenic acid in soybean seed. RG10 is a low linolenic acid line (<25 g/kg) produced by ethyl methane sulfonate (EMS) treatment of the low linolenic acid EMS mutant line C1640. The objective of this study was to determine the molecular basis of the low linolenic acid trait in RG10. Sequence analyses of mutant RG10 and wild‐type OX948 ω‐fatty acid desaturase (Fad3) genes showed that the low level of linolenic acid in RG10 is likely a result of mutations in two Fad3 genes. A mutation in the Fad3A gene introduces a stop codon in exon 6 that would prematurely terminate translation and a second mutation in the 5′ splice site of intron 5 of the Fad3B gene may result in abnormal mRNA splicing products. Both mutations would result in a non‐functional enzyme. Molecular markers developed for these mutations should simplify and accelerate introgression of the RG10‐based low linolenic acid trait into elite soybean cultivars.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.001

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.

Opus teacher head0.022
GPT teacher head0.202
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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

Citations52
Published2009
Admission routes1
Has abstractyes

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