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The Role of Selenium in Sheep Health

2025· article· en· W4416421695 on OpenAlexaffabout
Rebecka A. Sadler, Nicole Moran, Umesh K. Shandilya, E.S. Ribeiro, Bonnie A. Mallard, Amir Behzad Bazrgar, Niel A. Karrow

Bibliographic record

VenueAnimal Science Cases · 2025
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSeleniumImmunocompetenceLactationPregnancyAnimal healthColostrumHormoneMicronutrient

Abstract

fetched live from OpenAlex

Abstract Selenium (Se) is an essential mineral for animal health. Due to widespread soil Se deficiency in global regions, selenium must be supplemented in sheep diets to prevent harmful conditions such as white muscle disease (WMD). Previous research has also demonstrated the beneficial immunomodulatory properties of Se in improving antibody production and reducing inflammation. Beyond its role in disease prevention and immunomodulation, Se functions as a potent antioxidant, promotes thyroid hormone metabolism, and improves reproductive outcomes. Strategies for improving animal Se status can include adding Se to mineral premixes, salt blocks and feed additives, enriching crops with soil Se, and even Se injections. Se primarily comes in either organic or inorganic forms. Though organic Se is more bioavailable, it is costlier than inorganic Se. This case study evaluates the impact of organic versus inorganic Se supplementation on ewe Se status and immunocompetence in 110 Dorset-Rideau ewes during the late pregnancy and lactation periods. The sheep were located in Ponsonby, Ontario, Canada. Though immunocompetence was not significantly different between treatment groups, the ewes in the organic treatment groups had higher serum Se concentrations after trial day 40, showing the benefit of organic Se in improving Se status over time. Information © The Authors 2025

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.324
Teacher spread0.304 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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