Use of formic acid to control vibriosis in shrimp aquaculture
Bibliographic record
Abstract
Luminous vibriosis is a shrimp disease that causes major economic losses in shrimp industry as a result of massive shrimp kills due to bacterial infection caused by Vibrio species. Use of antibiotics to control Vibrio in shrimp aquaculture is not allowed in the United States and so it is necessary to develop an alternative pathogen control method for shrimp production. Short-chain fatty acids have been used as food preservatives for a long time. Organic acids are commonly added in feeds in animal production, such as chicken, pig, and cattle. In this study, growth inhibition effects of formic acid on five selected Vibrio species, namely Vibrio alginolyticus, Vibrio cholerae, Vibrio harveyi, Vibrio parahaemolyticus and Vibrio vulnificus were studied. The Vibrio bacteria were grown on both solid and liquid media using Muller-Hinton agar and alkaline peptone water, respectively, with various concentrations of formic acid. Bacterial growth was monitored in the liquid media using optical density method. The results showed significant inhibition of growth of all five Vibrio species by formic acid at low concentration. The effective concentration (EC50) values were calculated for all five Vibrio species, which were less than 0.039% of formic acid. The results are encouraging to supplement formic acid in the shrimp feed as a control mechanism to reduce Vibrio outbreak in shrimp aquaculture system.
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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".