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Record W2315719596 · doi:10.1021/jf102208a

Seed Coat Phenolics and the Developing Silique Transcriptome of Brassica carinata

2010· article· en· W2315719596 on OpenAlexaff
Xiang Li, Neil D. Westcott, Matthew G. Links, Margaret Y. Gruber

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSiliqueBrassica carinataCoatTranscriptomeBrassicaBiologyBotanyArabidopsis thalianaEcologyGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

Structures for nine compounds were elucidated in seed coats of two genetically related Brassica carinata lines. The yellow-seeded line accumulated monomeric kaempferols, phenylpropanoids, and lignans, while extractable and unextractable proanthocyanidins and a high-performance liquid chromatography peak containing polymeric-like quercetin/lignan structures were strongly reduced. The brown-seeded line accumulated large amounts of both types of proanthocyanidins (extractable and unextractable), as well as phenylpropanoids and lignans equivalent to the amounts in the yellow-seeded seed coats, but the brown-seeded seed coats lacked kaempferols. A Brassica napus 15K oligoarray experiment indicated that yellow-seeded siliques had more extreme gene expression changes and a 2.4-fold higher number of upregulated genes than brown-seeded siliques, including a host of transcription factors and genes with unknown function. Transcripts for six flavonoid genes (CHS, F3H, FOMT, DFR, GST, and TTG1) were lower and two (F3'H and FLS) were higher in yellow-seeded siliques, but expression of CHI, PAP1, and phenylpropanoid genes was unchanged.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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