Effect of quercetin supplementation on growth performance, nutrient digestibility, excreta bacterial count, noxious gas emission, and meat quality of broilers when fed different protein diets
Bibliographic record
Abstract
This study aimed to evaluate the effect of flavonoid supplementation to a low-protein diet (LCP) on growth performance, nutrient digestibility, excreta bacterial count, and excreta gas emission of broilers. In total, 800 one-day-old Ross 308 broilers (BW; 42.90 ± 1.43 g) were randomly sorted into one of the 4 dietary treatments (10 pens/treatment; 20 birds/pen). Treatment diets were high-protein diet (HCP), basal diet; LCP, basal diet − 2.5% protein; TRT1, LCP + 0.025% quercetin; and TRT2, LCP + 0.050% quercetin. Experimental diets were provided to broilers from days 8 to 35. HCP, TRT1, and TRT2 showed higher body weight gain (BWG) than the LCP group during days 8–21 and in the overall experiment. However, feed conversion ratio of the HCP group was improved than that of the LCP group during days 8–21 and in the overall period. The increasing level of quercetin supplementations brought a linear increase in BWG. Lactobacillus, Escherichia coli, and Salmonella counts in excreta samples of the experimental groups showed no significant difference. Flavonoid supplementation (0.050%) reduced drip loss in breast muscle more than that in the LCP group and showed a linear reduction. Through improved digestion, quercetin addition to an LCP reversed the BWG.
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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 | 0.001 |
| 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".