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Record W3087355088 · doi:10.3390/foods9091324

Assessment of Dietary Selenium and Vitamin E on Laying Performance and Quality Parameters of Fresh and Stored Eggs in Japanese Quails

2020· article· en· W3087355088 on OpenAlexaff
Zabihollah Nemati, Hosain Ahmadian, Maghsoud Besharati, Steven Lesson, Kazem Alirezalu, Rubén Domínguez, José M. Lorenzo

Bibliographic record

VenueFoods · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSeleniumLayingFood scienceBiologyAnimal scienceVitamin EVitaminBiotechnologyChemistryAntioxidantBiochemistryEngineering

Abstract

fetched live from OpenAlex

The effect of dietary supplementation with VE and Se on the laying productive performance, immunity, and the quality parameters of fresh and stored eggs was assessed. For this study, five treatments, namely control (basal diet), control plus 30 mg of VE and 0.4 mg kg−1 sodium selenite (VE30SS), control plus 30 mg of VE and 0.4 mg kg−1 of Sel-Plex® (VE30SP), control plus 120 mg VE and 0.4 mg kg−1 Sodium selenite (VE120SS), and control plus 120 mg VE and 0.4 mg kg−1 Sel-Plex (VE120SP), were examined. There was no huge impact of VE and Se on feed consumption, FCR and egg yield rate. Quality parameters of fresh egg including egg surface area, eggshell thickness, yolk selenium concentration, albumen height, and Haugh unit were significantly increased following VE and Se supplementation (p < 0.05). For stored eggs, VE and Se significantly increased egg yolk color intensity (p < 0.05). Regardless of storage temperature, eggs from birds fed with VE and Se had less weight loss during 30 days of storage. Albumen height was significantly higher in VE and Se fed birds in eggs stored at 5 °C for 15 and 30 days. The combination of Sel-Plex with either levels of VE had significantly higher blood total antioxidant capacity. Dietary VE and Selenium, notably Sel-Plex, improved the antioxidant potential of blood and egg quality of laying quails.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.099

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 head0.067
GPT teacher head0.302
Teacher spread0.235 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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