Effects of dietary garlic on cholesterol metabolism in laying hens
Bibliographic record
Abstract
Experiments were conducted to evaluate the potential for dietary garlic to influence egg yolk cholesterol concentrations and overall performance in different layer strains. Thirty-six, 28-wk-old, Hisex Brown,Isa Brown, Lohmann, Starcross, Babcock, and Starcross-579 strains (six hens per strain) were fed diets supplemented with 0 (control), 2, 4, 6, 8, or 10% sun-dried garlic paste for 6 wk. There were no differences (P > 0.05) among diets or strains in egg weight, egg mass, feed consumption, feed efficiency, and BW gain as averaged over 6 wk. Yolk weight, however, responded quadratically with increasing levels of dietary garlic and differed among strains (P < 0.05). Egg production and yolk weights were significantly higher in the Babcock strain in comparison with other strains. Serum and egg yolk cholesterol concentrations decreased linearly (P < 0.05) with increasing levels of dietary garlic. Serum and egg yolk cholesterol concentrations also differed among different strains (P < 0.05). It was concluded that garlic paste in the diets of laying hens reduced serum and yolk cholesterol concentrations. It was also concluded that dietary garlic paste had no adverse effects on layer performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".