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Record W1989664077 · doi:10.3382/ps.2009-00571

Selenium enrichment of table eggs

2010· article· en· W1989664077 on OpenAlexaff
Danielle Bennett, Ke Cheng

Bibliographic record

VenuePoultry Science · 2010
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSeleniumDietary Reference IntakeAnimal scienceBiologyReference Daily IntakeEggshellHuman nutritionFood scienceChemistryNutrientEcology

Abstract

fetched live from OpenAlex

Selenium is an essential trace element with a recommended dietary allowance for human adults of 55 μg/d. However, there is evidence that greater dietary intakes may have possible health benefits, including a reduction in the risk of cancer. Several studies have shown the feasibility of enriching eggs using organic Se and that Se-enriched eggs are an effective way to supplement human diets. However, few studies have examined the response of egg Se concentration to high (>1 μg/g) dietary organic Se intake by the laying hens. The objective of the current study is to examine the effect of higher dietary organic Se levels on production, egg mass, and egg Se levels. These were assessed by feeding 3 breeds of laying hens (Barred Plymouth Rock, Lohmann Brown, Lohmann White) a basal diet containing 0.3 μg of Se/g of diet as Na2SeO3. Into this diet, Se yeast (SelenoSource AF 600), an organic source of Se, was added at 1.0, 2.4, or 5.1 μg of Se/g of diet for 4 wk. Feed consumption, egg production, and egg mass were not affected by the dietary Se concentration in all 3 breeds. Within the range of Se levels employed in the laying hens' diet, egg Se content increased linearly as dietary levels of Se increased. The results of this study indicate that feeding up to 5.1 µg/g of Se will not affect egg production and the welfare of the laying hen and is a practical way of producing Se-enriched eggs for the consumers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0040.001

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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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 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

Citations52
Published2010
Admission routes1
Has abstractyes

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