Evaluation of dried vegetable residues for poultry: III Effects of feeding cabbage leaf residues on laying performance, egg quality, and apparent total tract digestibility
Bibliographic record
Abstract
The objective of the present study was to investigate the effects of feeding dried cabbage leaf residues (DCR) on egg production parameters, egg components, and egg fatty acid, cholesterol, and α-tocopherol concentrations and apparent total tract digestibility (ATTD). Seventy-two 34-week-old layers were randomly allotted to 4 dietary treatments: 0, 4, 8, or 12% DCR with 6 replicates with 3 birds each. Results showed that feed intake, egg production, and feed conversion ratio were unaffected by dietary treatments. Similar treatment effects also were observed for egg yolk and albumin percentages. However, eggshell percentage decreased (quadratic effect, P = 0.008) with increasing DCR. Egg yolk concentrations of α-tocopherol (linear effect, P <0.001), polyunsaturated fatty acids (linear effect, P = 0.0288), and linolenic acid (quadratic effect, P = 0.025) increased with increasing dietary DCR. However, inclusion of DCR had no influence on egg yolk cholesterol concentration. ATTD of dry matter (quadratic effect, P = 0.005), organic matter (quadratic effect, P = 0.008), neutral detergent fiber (quadratic effect, P = 0.0012), apparent metabolizable energy (quadratic effect, P = 0.0006), and apparent metabolizable energy corrected-nitrogen (quadratic effect, P = 0.0006) increased as the level of DCR in the diet increased. However, ATTD of crude protein and gross energy was similar among treatments. It was concluded that DCR can be fed to layers up to 12% without adverse effects on production parameters and may improve total tract nutrient utilization and egg quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".