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Record W2519527003 · doi:10.1139/cjm-2016-0436

Quantitative analysis of 3-OH oxylipins in fermentation yeast

2016· article· en· W2519527003 on OpenAlexafffundvenue
Greg Potter, Wei Xia, Suzanne M. Budge, R. Alex Speers

Bibliographic record

VenueCanadian Journal of Microbiology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsDalhousie University
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of Canada
KeywordsOxylipinFermentationYeastChemistryFood scienceStrain (injury)SugarChromatographyBiochemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

Despite the ubiquitous distribution of oxylipins in plants, animals, and microbes, and the application of numerous analytical techniques to study these molecules, 3-OH oxylipins have never been quantitatively assayed in yeasts. The formation of heptafluorobutyrate methyl ester derivatives and subsequent analysis with gas chromatography - negative chemical ionization - mass spectrometry allowed for the first determination of yeast 3-OH oxylipins. The concentration of 3-OH 10:0 (0.68-4.82 ng/mg dry cell mass) in the SMA strain of Saccharomyces pastorianus grown in laboratory-scale beverage fermentations was elevated relative to oxylipin concentrations in plant tissues and macroalgae. In fermenting yeasts, the onset of 3-OH oxylipin formation has been related to fermentation progression and flocculation initiation. When the SMA strain was grown in laboratory-scale fermentations, the maximal sugar consumption rate preceded the lowest concentration of 3-OH 10:0 by ∼4.5 h and a distinct increase in 3-OH 10:0 concentration by ∼16.5 h.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.240
Teacher spread0.213 · 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

Citations2
Published2016
Admission routes3
Has abstractyes

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