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Record W2059152400 · doi:10.2527/af.2012-0013

Selenium in milk and human health

2014· article· en· W2059152400 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

VenueAnimal Frontiers · 2014
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Prince Edward Island
FundersUniversidad de CaldasMassey UniversityNorth Carolina State UniversityIowa State UniversityDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsSeleniumBioavailabilitySodium selenateFood scienceSelenateHuman healthChemistryEnvironmental healthBiologyMedicineBioinformatics

Abstract

fetched live from OpenAlex

Selenium in vegetables, milk, and meat is a highly bioavailable source for humans; further, foods that are intentionally Se enriched can significantly enhance the consumption of Se by humans where Se intake would be otherwise suboptimal. Selenium concentration in milk can be easily manipulated by altering Se supply to dairy cows. Transfer in milk of Se from yeast is more efficient than from inorganic sources, such as sodium selenite/selenate. There is a U-shaped risk response to Se intake, wherein supra-nutritional intakes (i.e., intakes greater than those recommended to meet metabolic requirements) can in some instances increase the risk of disease in individuals with adequate Se status. A better understanding of the interacting factors that affect bioavailability and metabolism, including the effects of Se form, con-current nutritional factors, and physiological state, will be important in establishing more refined recommendations for Se intakes.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.437

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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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