Antimicrobial Action of Epidermal Mucus Extract of Clarias gariepinus (Burchell, 1822) Juveniles-Fed Ginger Inclusion in Diet
Bibliographic record
Abstract
The antimicrobial activity of epidermal mucus extract of C. gariepinus juveniles-fed ginger inclusion in diet was investigated and compared with the activity of epidermal mucus extract of C. gariepinus juveniles (control) without ginger in diet. This study demonstrates the antimicrobial role of ginger in improving protection of fish against bacterial infection as shown by the higher zones of inhibition observed for epidermal mucus extract of fish-fed ginger in diet as compared with control. Zones of inhibition for epidermal mucus of treatment fish were 30.7 mm, 29.8 mm, 26.3 mm and 19.3 mm for Bacillus, Escherichia, Staphylococcus and Streptococcus species respectively. Though these values were not significantly (P > 0.05) higher than those obtained for the control fish with zones of inhibition of 25 mm, 11.2 mm, 9.0 mm and 7.3 mm for Bacillus, Escherichia, Staphylococcus and Streptococcus species respectively, the higher values recorded for the treatment fish shows that ginger inclusion in fish diet had an antibiotic effect against isolates of bacteria in fish samples from cultured ponds. The addition of ginger in C. gariepinus diet is encouraged as its action is indicative of the potentials of ginger in preventing emergence of resistant bacteria and improving the antimicrobial role of fish mucus and therefore the quality of C. gariepinus.
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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".