The potentials of fructooligosaccharide on growth, feed utilization, immune and antioxidant parameters, microbial community and disease resistance of tilapia (<i>Oreochromis niloticus</i> × <i>O. aureus</i>)
Bibliographic record
Abstract
This study was conducted to investigate the potentials of dietary fructooligosaccharide (FOS) on growth and health of tilapia. Fish (5.00 ± 0.02 g) were fed five diets with FOS supplementation at 0 (CON), 0.5 (F-1), 1 (F-2), 2 (F-3) and 4 (F-4) g/kg feed for 8 weeks. The results showed that the weight gain and feed conversion ratio were improved by F-2, F-3 and F-4 groups (p < .05). Dietary FOS promoted the retentions of protein and lipid (p < .05), and the digestibility of dry matter and protein was encouraged by F-2 and F-3 groups (p < .05). The FOS supplementation (except F-4 group) increased serum alkaline phosphate and acid phosphatase activities and hepatic superoxide dismutase activity (p < .05). The amylase activity and villus height in anterior intestine were enhanced by all FOS groups (p < .05). The numbers of total bacteria in F-3 and F-4 groups, lactic acid bacteria in F-4 group and Bacillus in all FOS groups were increased (p < .05). After challenged with Aermonas hydrophila, the cumulative mortality was decreased by all levels of FOS supplementation (p < .05). In conclusion, the FOS supplementation at least 1 g/kg feed could promote growth, nutrient utilization, immune and antioxidant parameters, digestive enzyme activity, gut health and disease resistance of tilapia.
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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".