Assessing the response of hen weight, body composition, feather score, egg quality, and level of excreta nitrogen content to digestible balanced protein intake of laying hens
Bibliographic record
Abstract
Ideally balanced dietary protein is critical for laying hen egg production and feed efficiency, but also affects other important characteristics. This research was designed to study the nonegg production and feed intake response of Lohmann-LSL Lite hens to 550, 625, 700, 775, and 850 mg d−1 of amino acid balanced digestible lysine (Dlys) from 27 to 66 wk of age. Data collection included hen weight (HW), feather scoring, tissue weights, egg specific gravity, egg component weights, and excreta nitrogen (N) content. The experiment was a completely randomized design and level of significance was fixed at P ≤ 0.05. Hen weight (quadratic, Q), pectoralis muscle (absolute, % – Q), and abdominal fat (absolute – linear, L; % – Q) increased with increasing Dlys intake. Although gastrointestinal segment weights and lengths were affected by Dlys intake, interpretation of results was confounded by other aspects of diet composition. Hen feather score (L) and excreta N content (Q) increased, and egg shell quality (L) decreased with increasing Dlys intake. Absolute egg component weights increased with Dlys intake, but effects on proportional weights were Q and relatively minor. In conclusion, balanced Dlys intake affected a variety of practical nonproduction characteristics in laying hens.
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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".