Assessment of Dietary Selenium and Vitamin E on Laying Performance and Quality Parameters of Fresh and Stored Eggs in Japanese Quails
Bibliographic record
Abstract
The effect of dietary supplementation with VE and Se on the laying productive performance, immunity, and the quality parameters of fresh and stored eggs was assessed. For this study, five treatments, namely control (basal diet), control plus 30 mg of VE and 0.4 mg kg−1 sodium selenite (VE30SS), control plus 30 mg of VE and 0.4 mg kg−1 of Sel-Plex® (VE30SP), control plus 120 mg VE and 0.4 mg kg−1 Sodium selenite (VE120SS), and control plus 120 mg VE and 0.4 mg kg−1 Sel-Plex (VE120SP), were examined. There was no huge impact of VE and Se on feed consumption, FCR and egg yield rate. Quality parameters of fresh egg including egg surface area, eggshell thickness, yolk selenium concentration, albumen height, and Haugh unit were significantly increased following VE and Se supplementation (p < 0.05). For stored eggs, VE and Se significantly increased egg yolk color intensity (p < 0.05). Regardless of storage temperature, eggs from birds fed with VE and Se had less weight loss during 30 days of storage. Albumen height was significantly higher in VE and Se fed birds in eggs stored at 5 °C for 15 and 30 days. The combination of Sel-Plex with either levels of VE had significantly higher blood total antioxidant capacity. Dietary VE and Selenium, notably Sel-Plex, improved the antioxidant potential of blood and egg quality of laying quails.
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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) | 0.000 | 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".