Influence of dietary lysolecithin on growth performance, nutrient digestibility, haemato-biochemistry, and oxidative status of broiler birds
Bibliographic record
Abstract
This study examined the effect of dietary lysolecithin on growth performance, nutrient digestibility, haematobiochemistry, and oxidative status in finisher broiler chickens. In a completely randomized design (CRD), 300 21-day-old Chikun strain chicks were randomly allocated to 30 pens in which they were allotted to 5 dietary treatments as follows TI (0 g /100kg), T2 (100 g /100kg), T3 (200 g /100kg), T4 (300 g /100kg), T5 (400 g /100kg) of feed with 6 replicates pens of 10 during the finisher phase (28 days). Results showed that dietary supplementation with lysolecithin increased the final body weight (FBW) (quadratic P = 0.0178), body weight gain (BWG) (quadratic P = 0.0232), whilst it decreased the total feed intake (TFI) (linear P = 0.0104). Similarly, it linearly increased the retention of dry matter (P = 0.0324); crude protein (P = 0.0029), crude fibre (P = 0.0147), and crude fat (P = 0.0002). Furthermore, it increased the superoxide dismutase (linear P < 0.001), glutathione peroxidase (quadratic P < 0.001), glutathione (linear P < 0.001), whilst it decreased malondialdehyde (linear P = 0.003), without affecting (P > 0.05) the haematobiochemistry parameters. Therefore, dietary lysolecithin could be supplemented up to 400 g /100 kg without compromising performance, nutrient retention, haemato-biochemistry, and oxidative status in finisher broiler diets.
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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".