Antimicrobial Use and Resistance in Aquaculture: Findings of a Globally Administered Survey of Aquaculture‐Allied Professionals
Bibliographic record
Abstract
There is limited published information regarding antimicrobial use (AMU) and antimicrobial resistance (AMR) in aquaculture. Our objective was to determine the opinions of aquaculture-allied professionals around the world on the frequency of AMU and AMR in common aquatic species. The study questionnaire included five sections: respondent demographics, extent of AMU in aquaculture, frequency of observations of AMR in aquaculture, AMR monitoring and surveillance and antimicrobial susceptibility testing in various jurisdictions. It was administered in English and Spanish to 604 professionals in 25 countries and with varying expertise in aquaculture. The response rate was 33% (199/604). Over half of the participants had >10 years of experience in aquaculture: 70% (140/199) were involved in fish health/clinical work and their primary experience was with salmon, tilapia, trout, shrimp (including prawn) and/or catfish. Tetracycline use was reported by 28%, 46%, 18%, 37% and 9% of respondents working with catfish, salmon, tilapia, trout and shrimp, respectively. Resistance to tetracycline in one or more species of bacteria was reported as 'frequent-to-almost always' for the same aquaculture species by 39%, 28%, 17%, 52% and 36% of respondents, respectively. 'Frequent-to-almost always' use of quinolone was reported by 70% (32/46) and 67% (8/12) of respondents from the United States and Canada, respectively, where quinolone products are not approved for aquaculture, and extra-label fluoroquinolone use is either prohibited (United States) or discouraged (Canada). Similar frequencies of quinolone use were also reported by the majority of respondents from Europe [70% (7/10)] and Asia [90% (9/10)] where labelled indications exist. This baseline information can be used to prioritize research or surveillance for AMU and AMR in aquaculture.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".