Effect of Galangal (Alpinia galanga Linn.) Extract on the Growth Rate and Resistance to Vibrio harveyi and White Spot Diseases in Pacific White Shrimp (Litopenaeus vannamei)
Bibliographic record
Abstract
The anti-microbial activity of galangal (Alpinia galanga Linn.) is well known. In this study, the feeding of galangal crude extract was investigated for its effect on preventing the infectious diseases Vibrio harveyi and white spot syndrome virus in Pacific white shrimp (Litopenaeus vannamei). A commercial diet mixed with galangal ethanol extract was fed to shrimp for 1 or 2 months. In the first month of feeding, the growth rate of the galangal extract diet group was lowered compared with that of the control diet group, possibly because the shrimp required time to acclimatize to the galangal diet. After 2-months of feeding, the growth of the shrimp in terms of body weight, specific growth rate and survival rate of the galangal diet group did not differ significantly (P > 0.05) from that of the control diet group. The clearance ability was evaluated by counting the bacterial cells in the hemolymph of shrimp injected with V. harveyi in the abdomenal segment. The number of V. harveyi in the hemolymph of the galangal diet group was significantly lower than that in the control diet group (P < 0.05), indicating the higher clearance ability of the galangal diet group. The oral administration of galangal extract enhanced the resistance of Pacific white shrimp against V. harveyi and white spot syndrome diseases, as demonstrated by the significantly higher survival rate of the galangal diet group. These results suggested that galangal is useful as an alternative to chemotherapeutic treatment to solve the problems created by residual antibiotics in shrimp.
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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".