Farm Level and Geographic Predictors of Antibiotic Use in Sri Lankan Shrimp Farms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it