Effects of dietary fish silage and fish fat on performance and egg quality of laying hens
Bibliographic record
Abstract
A total of 45 laying hens were fed a control diet, or one of four diets containing 50 g kg–1 fish silage and different levels of fish fat (1.8, 8.8, 16.8 or 24.8 g kg–1), to determine the effect of fish silage and fish fat in the diet on performance and egg quality. Fish silage did not affect feed intake, egg production, fatty acid composition of yolk, yolk color or sensory quality of eggs, compared with the control. The diets with 16.8 or 24.8 g kg–1 fish fat decreased feed intake (P < 0.001), egg production (P < 0.001), and hen-day egg production (P < 0.04), and increased yolk color index (P < 0.003). The proportions of the fatty acid C22:1 (P < 0.001), and PUFA as the sum of C18:2 n-6, C20:5 n-3, C22:5 n-3 and C22:6 n-3 (P < 0.02) in egg yolk were highest for the fish silage diets with 24.8, 16.8 or 8.8 g kg–1 fish fat, and lowest for the diet with 1.8 g kg–1 fish fat. Proportions of C18:1 (P < 0.001) and C20:1 (P < 0.001) were lowest for the diets with 16.8 or 24.8 g kg–1 fish fat. Egg yolk cholesterol did not differ among treatments. The diet with 16.8 g kg–1 fish fat resulted in a more intense egg albumen whiteness as measured by the sensory study, compared with the other diets (P < 0.05). There was a linear relationship between dietary fish fat level and increased off-taste intensity of egg yolk (P< 0.03). Key words: Fish silage, fish fat, laying hens, egg production, 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) | 0.000 | 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".