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Record W2336859372 · doi:10.1371/journal.pone.0152709

Selenium Digestibility and Bioactivity in Dogs: What the Can Can, the Kibble Can’t

2016· article· en· W2336859372 on OpenAlexaboutno aff
Marielle Van Zelst, Myriam Hesta, Kerry Gray, Karen Beech, An Cools, Lucille Alexander, Gijs Du Laing, Geert Janssens

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsnot available
FundersUniversiteit Gent
KeywordsSeleniumFood scienceChemistryGlutathione peroxidaseAnimal scienceBiologyBiochemistryGlutathioneEnzyme

Abstract

fetched live from OpenAlex

There is a growing concern for the long-term health effects of selenium (Se) over- or underfeeding. The efficiency of utilization of dietary Se is subject to many factors. Our study in dogs evaluated the effect of diet type (canned versus kibble) and dietary protein concentration on Se digestibility and bioactivity. Canned and kibble diets are commonly used formats of dog food, widely ranging in protein concentration. Twenty-four Labrador retrievers were used and four canned and four kibble diets were selected with crude protein concentrations ranging from 10.1 to 27.5 g/MJ. Crude protein concentration had no influence on the digestibility of Se in either canned or kibble diets, but a lower Se digestibility was observed in canned compared to kibble diets. However, the biological activity of Se, as measured by whole blood glutathione peroxidase, was higher in dogs fed the canned diets than in dogs fed the kibble diets and decreased with increasing crude protein intake. These results indicate that selenium recommendations in dog foods need to take diet type into account.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.055
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.055
GPT teacher head0.239
Teacher spread0.184 · 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 designObservational
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

Citations12
Published2016
Admission routes1
Has abstractyes

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