Influence of the housing system on carcass composition and meat quality of laying hens after the laying period
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".