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Record W2101950781 · doi:10.4141/cjas07102

Effects of supplementing layer hen diets with selenium and vitamin E on egg quality, lipid oxidation and fatty acid composition during storage

2008· article· en· W2101950781 on OpenAlexvenueno aff
M. Mohiti-Asli, F. Shariatmadari, H Lotfollahian, Mohamad Taghi Mazuji

Bibliographic record

VenueCanadian Journal of Animal Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsSeleniumVitamin EFood scienceYolkChemistryFatty acidLipid peroxidationMalondialdehydeVitaminLipid oxidationComposition (language)AntioxidantPolyunsaturated fatty acidTocopherolBiochemistry

Abstract

fetched live from OpenAlex

A 7-wk trial was carried out to investigate the effect of vitamin E and inorganic and organic selenium added to hens' diet on quality and lipid stability of eggs during storage. One hundred forty-four Hy-Line W-36 hens (63-wk of age) were divided into six equal groups. Five groups received a basal diet supplemented with 0.4 mg kg -1 sodium selenite or selenium yeast, 200 mg kg -1 vitamin E or a combination of selenium and vitamin E; whereas the control group received no supplementation. Hen production was assessed daily and fresh egg quality parameters were determined every 2 wk. Eggs were stored under different conditions (4°C, 23–27°C or 31°C) for 14 d. Eggs were analyzed for quality characteristics, egg component weight, Malondialdehyde values as a secondary oxidation product and yolk fatty acid (FA) composition. The performance of the hens and egg weights were not affected either by the source of the selenium or by the vitamin E. The inclusion of selenium or vitamin E in the diet significantly increased their concentrations in the egg. The supplemented diets also improved egg quality, oxidative stability and fatty acid composition during storage. Key words: Vitamin E, selenium, lipid peroxidation, egg fatty acid composition, storage

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.296

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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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

Citations82
Published2008
Admission routes1
Has abstractyes

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