Antibiotic resistance genes in the aquaculture sector: global reports and research gaps
Bibliographic record
Abstract
Aquaculture has been one of the fastest-growing food production systems over the last decade and increased intensification of production has created conditions that favour disease outbreaks. Antibiotics are commonly applied in the food animal sector to fight against bacterial infections; however, their inappropriate use contributes to the emergence of antibiotic-resistant bacteria. Investment in research and capacity-strengthening, in parallel with enforcing existing regulations around antimicrobial use, are potentially powerful tools in tackling the threat of antimicrobial resistance (AMR) emanating from animal producing systems such as aquaculture. However, directing investment effectively is challenging due to the limited data available that hinder the identification of risk areas for current and future AMR emergence. Here, we aim to partially fill this gap by analyzing the current peer-reviewed literature reporting antibiotic resistance genes (ARGs) in aquaculture food production systems and combining the data in a systematic map. Systematic searches of three bibliographic databases, a search engine, and 120 reviews returned 10 699 articles that were screened at title and abstract and then by full text (n = 1100). Two hundred and eighteen articles, spanning 39 countries and six continents, met all inclusion criteria and were coded to retrieve bibliographic, methodology, and study outcome data. ARG detections were associated with 44 families of fish and crustaceans and 75 genera of bacteria, with most studies employing primer-based methods to detect ARGs. A narrative synthesis explores implications for future research and policy as well as limitations of the systematic mapping methodology.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".