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Record W4406320702 · doi:10.1016/j.psj.2025.104817

Influence of the housing system on carcass composition and meat quality of laying hens after the laying period

2025· article· en· W4406320702 on OpenAlexaff
Marcin Wegner, Dariusz Kokoszyński, Joanna Żochowska‐Kujawska, Marek Kotowicz, Joanna Frischke-Krajewska

Bibliographic record

VenuePoultry Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsLayingQuality (philosophy)Period (music)Animal scienceComposition (language)Food scienceBusinessBiologyEngineering

Abstract

fetched live from OpenAlex

The purpose of the present study was to compare the effects of two systems of keeping Lohmann Brown laying hens in terms of carcass composition and meat quality assessment (chemical composition, physicochemical properties, texture, rheological properties, microstructure). The experimental material consisted of 30 carcasses (15 litter system - SS, 15 aviary system - SW) of Lohmann Brown laying hens after laying at 85 weeks of age. After slaughtering, the weight of the eviscerated carcass and the proportion of carcass elements, the weight of selected internal organs, acidity (pH 24 ), and electroconductivity (EC 24 ), color (L*, a*, b*), and the basic chemical composition of pectoral muscles and leg muscles were determined. The texture, rheological properties, and microstructure of the pectoralis major muscle were analyzed. The housing system affected the percentage of abdominal fat (SW - 2.10% vs . SS - 0.81%) and skin with subcutaneous fat (SW - 11.84% vs. SS - 10.89%). There were also differences in the weight of the giblets (proventriculus, gizzard, liver, heart). The housing system also affected the fiber cross-sectional area (SW - 660.01 µm 2 vs . SS - 674.53 µm 2 ). The present study provided information on the differences in some carcass traits and the quality of meat of Lohmann Brown hens depending on the housing system.

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 score0.618
Threshold uncertainty score0.295

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 studies0.0000.001
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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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