Effects of Deinococcus spp. supplement on egg quality traits in laying hens
Bibliographic record
Abstract
To counter the ill effects of synthetic dyes, bacterial pigment production as an alternative is now one of the promising and emerging fields of research. This study was conducted to evaluate the applicability of Deinococcus genus on the egg quality traits in laying hens. In study I, 24 single comb White Leghorn layers were fed with various 1 wt % Deinococcus bacterial strains for 10 d. In study II, 84 brown Hendrix layers were fed with one of 4 diets containing 0, 0.2, 1, or 5 wt % Deinococcus sp. GKB-Aid 1995 powder for 12 wk. In study III, 60 White Leghorn laying hens were fed either with or without 1 wt % Deinococcus sp. GKB-Aid 1995 powder, 1 wt % Deinococcus sp. GKB-Aid 1995 granules, or 1 wt % Deinococcus sp. GKB-Aid 1995 oily granules for 10 successive d. In all of the experiments, feeding Deinococcus powder did not affect egg quality traits except for the yolk color. In particular, supplementation with all Deinococcus powder treatments changed the yolk color (P < 0.05) in study I, with the best pigmentation score obtained by D. grandis and Deinococcus sp. GKB-Aid 1995. Moreover, longer supplementation of Deinococcus sp. GKB-Aid 1995 in study II had a significant effect on feed conversion ratio. With these findings under consideration, the present study suggests that the Deinococcus species, especially Deinococcus sp. GKB-Aid 1995, can be an excellent candidate for improving egg yolk color 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".