Bibliographic record
Abstract
Received: 2018-05-06 | Accepted: 2018-05-14 | Available online: 2018-11-26 https://doi.org/10.15414/afz.2018.21.04.138-141 By modifying meals for hens it is possible to influence the content of selenium and lutein in eggs, which enables the production of eggs with an increased share of the desired functional ingredients. Such eggs on the market represent enriched or functional foods that are characterized by preventive action in order to preserve the human health. The results of our research show that the composition of meals affects the content of selenium and lutein in eggs. The use of Se-yeast in hensâ mixtures in the amount of 0.5 mg·kg of feed increases the selenium content in egg whites and yolks by 62.94% and 41.54% in comparison to eggs from hens fed with a conventional mixture. Addition of 400 mg·kg of lutein to a hens' mixture can enrich egg yolk with lutein by 86.93% with respect to eggs from hens fed with a conventional mixture (without the addition of lutein).By designing hensâ mixtures using selenium and lutein having antioxidant activity, it is possible to produce eggs with improved nutritional value and extended shelf life. Keywords: eggs, enrichment, lutein, selenium References Aljamal, A.A., Purdum, S.E., Hanford, K.J. (2014) The effect of normal and excessive supplementation of selenomethionine and sodium selenite in laying hens. International Journal of Applied Poultry Research, 3 (3), 33-38. Chung, H.Y., Rasmussen, H.M., Johnson, E.J. (2004) Lutein bioavailability is higher from lutein-enriched eggs than from supplements and spinach in men. The Journal of Nutrition, 134 (8), 1887-1893. DOI: https://dx.doi.org/10.1093/jn/134.8.1887 FaÅ¡iangová M., BoÅilová G., Hulánková R. 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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".