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Record W3175694909 · doi:10.1093/mtomcs/mfab031

Compilation of selenium metabolite data in selenized yeasts

2021· review· en· W3175694909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMetallomics · 2021
Typereview
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSeleniumMetaboliteSaccharomyces cerevisiaeYeastChemistryMetabolomicsBiochemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Selenium-enriched yeast has long been recognized as an important nutritional source of selenium and studies have suggested that supplementation with this material provides chemo-preventative benefits beyond those observed for selenomethionine supplementation, despite the fact that selenomethionine accounts for 60-84% of the total selenium in selenized yeasts. There is much ongoing research into the characterization of the species comprising the remaining 16-40% of the selenium, with nearly 100 unique selenium-containing metabolites identified in aqueous extracts of selenized yeasts (Saccharomyces cerevisiae). Herein, we discuss the analytical approaches involved in the identification and quantification of these metabolites, and present a recently created online database (DOI: 10.4224/40001921) of reported selenium species along with chemical structures and unique mass spectral features.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.173
GPT teacher head0.392
Teacher spread0.219 · 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