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Record W4407019358 · doi:10.1139/cjas-2024-0051

Selenium and vitamin E status of horses in Quebec

2025· article· en· W4407019358 on OpenAlexafffundvenueabout
Erika David-Dandurand, Dany Cinq-Mars, Younès Chorfi

Bibliographic record

VenueCanadian Journal of Animal Science · 2025
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversité LavalUniversité de Montréal
FundersMitacs
KeywordsSeleniumVitamin EVitaminAnimal scienceBiologyGeographyChemistryEndocrinologyBiochemistryAntioxidant

Abstract

fetched live from OpenAlex

The objectives of this study were to measure plasma Se and vitamin E in horses across Quebec and survey their consumption. Four samplings were done on 59 horses for 1 year ( n = 224). Plasma was analyzed for Se, vitamin E, and glutathione peroxidase (GSH-Px). Hay, concentrate, and water were also analyzed for Se. Horses were classified into four groups for statistical analysis based on dietary forage to concentrate ratio: HF—high forage; MHF—moderately high forage; MC—moderate concentrate and HC—high concentrate. Owners were asked to answer a survey at each visit. Results demonstrate that plasma Se varies with body condition score (BCS) ( P = 0.019), season ( P = 0.015), and feeding groups ( P < 0.001). Plasma vitamin E was affected by BCS ( P = 0.04), sex ( P = 0.047), exercise ( P < 0.001), and season ( P < 0.001), while GSH-Px activity was influenced by BCS ( P = 0.018), exercise ( P < 0.001), and feeding groups ( P = 0.002). This study confirms that plasma Se and vitamin E reflect animal supplementation. Other parameters such as BCS, sex, season, exercise, and ration are important for precise recommendations. Moreover, the survey revealed that feeding practices of horses in Quebec are inadequate to meet Se and vitamin E requirements; therefore, owner's education should be part of the nutrition plan.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.268
Teacher spread0.252 · 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 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

Citations0
Published2025
Admission routes4
Has abstractyes

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Same venueCanadian Journal of Animal ScienceSame topicSelenium in Biological SystemsFrench-language works237,207