Farm Level and Geographic Predictors of Antibiotic Use in Sri Lankan Shrimp Farms
Bibliographic record
Abstract
Black tiger shrimp Penaeus monodon farming is important for Sri Lanka's rural development plans. Consumer confidence is critical for the development and maintenance of export and domestic shrimp markets. Public concern about the use of antimicrobial drugs and chemicals on shrimp farms, however, could threaten market access. We sought to identify high-risk areas and farm-level risk factors for antimicrobial use to inform the core messages and strategic placement of extension programs to help farmers develop best management practices for antimicrobial use. We undertook a survey of 603 operating farms within the Puttalam district over 42 weeks. Lower stocking density and early harvest were associated with a lower risk of antimicrobial use, whereas standard management practices, including water treatment, feed supplements, probiotic use, pond fertilizing, disinfectant use, and pesticide use, were associated with increased risk. Spatial cluster detection found three significant clusters of antimicrobial-using farms. Antimicrobials were more likely to be used in areas with lower farm density. Some of our counterintuitive findings are discussed from a socioecological perspective. A comprehensive understanding of why antimicrobials are used on shrimp farms requires an evaluation of the physical, epidemiological, and socioeconomic factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".