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Record W4401091148 · doi:10.1080/09712119.2024.2381729

The effects of <i>Aronia melanocarpa</i> (AM) dietary supplementation on production performance, meat and egg quality, yolk volatile substances and antioxidant capacity of laying hens

2024· article· en· W4401091148 on OpenAlexaff
Bo Jing, Haoyuan Wu, Zhenhua Liu, Hongmei Shang, Yuting Li, Zhouyu Jin, Hui Song

Bibliographic record

VenueJournal of Applied Animal Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsMinistry of Education and Child Care
FundersPeople's Government of Jilin Province
KeywordsYolkFood scienceAntioxidant capacityChemistryAntioxidantBiochemistry

Abstract

fetched live from OpenAlex

The effects of Aronia melanocarpa (AM) dietary supplementation on production performance, meat and egg quality, yolk volatile substances and antioxidant capacity of laying hens. A total of 480 Roman brown laying hens aged 25 wks were randomly divided into control, LAM, MAM and HAM groups and fed 0%, 1%, 4% and 7% AM, respectively. The results showed that AM dietary supplementation significantly increased the eggshell strength, significantly reduced 24 h dripping loss of breast and thigh muscles, and increased pH and 48 h dripping loss of breast and thigh muscles. Cooking loss was significantly lower only in the HAM group for breast muscle. The MAM group was able to significantly increase albumen height and Haugh units. Esters gradually decreased with the increase in AM content, and the addition of AM led to a decrease in alcoholic compounds but increased the contents of alkenes, alkanes, ketones, and acids. The dietary AM supplementation significantly reduces the MDA content of serum, liver, ovary, chest muscle, thigh muscle and yolk and increases the activities of GSH-Px, T-SOD and T-AOC. In summary, dietary AM supplementation could improve antioxidant capacity and expression of related genes, thereby improving meat and egg quality.

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)0.000
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 score0.854
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.333
Teacher spread0.265 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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