Efficacy of yeast and garlic extract mixture on growth performance, tract digestibility, excreta microbiota, gas emission, blood profile, and meat quality in broiler
Bibliographic record
Abstract
This experiment was accompanied to determine the use of yeast and garlic extract mixture in broiler diets on growth performance, nutrient absorption, excreta microbiota, blood profiles, and meat quality. A total of 792 male Ross 308 broilers (1-day-old, body weight 41 ± 0.5 g, and 5 weeks trial) were randomly allocated. Birds were arbitrarily assigned to one of four nutritive treatments (11 replicates; 18 birds/replicate). A basal diet (CON) was supplemented with 0.1%, 0.2%, and 0.3% yeast–garlic mixture (YGM). Body weight gain linearly increased during the overall period and tended to increase from day 21 to day 35, while feed intake showed a tendency to increase during the overall period by YGM inclusion. However, Salmonella counts linearly decreased, but Lactobacillus and Escherichia coli counts remained unaffected. Excreta CO2 emissions were linearly reduced; nevertheless, other noxious gas emissions were not affected. Furthermore, YGM supplementation elicited a tendency for improved lymphocytes and linearly increased IgG. However, feed conversion ratio, mortality, nutrient utilization, and meat quality were not influenced. YGM addition (0.1%, 0.2%, and 0.3%) linearly improved broiler growth performance by decreasing microbiota and gas emission and increasing blood parameters. So, the proper dose of YGM was 0.3%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".