Unpacking factors influencing antimicrobial use in global aquaculture and their implication for management: a review from a systems perspective
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
Global seafood provides almost 20% of all animal protein in diets, and aquaculture is, despite weakening trends, the fastest growing food sector worldwide. Recent increases in production have largely been achieved through intensification of existing farming systems, resulting in higher risks of disease outbreaks. This has led to increased use of antimicrobials (AMs) and consequent antimicrobial resistance (AMR) in many farming sectors, which may compromise the treatment of bacterial infections in the aquaculture species itself and increase the risks of AMR in humans through zoonotic diseases or through the transfer of AMR genes to human bacteria. Multiple stakeholders have, as a result, criticized the aquaculture industry, resulting in consequent regulations in some countries. AM use in aquaculture differs from that in livestock farming due to aquaculture's greater diversity of species and farming systems, alternative means of AM application, and less consolidated farming practices in many regions. This, together with less research on AM use in aquaculture in general, suggests that large data gaps persist with regards to its overall use, breakdowns by species and system, and how AMs become distributed in, and impact on, the overall social-ecological systems in which they are embedded. This paper identifies the main factors (and challenges) behind application rates, which enables discussion of mitigation pathways. From a set of identified key mechanisms for AM usage, six proximate factors are identified: vulnerability to bacterial disease, AM access, disease diagnostic capacity, AMR, target markets and food safety regulations, and certification. Building upon these can enable local governments to reduce AM use through farmer training, spatial planning, assistance with disease identification, and stricter regulations. National governments and international organizations could, in turn, assist with disease-free juveniles and vaccines, enforce rigid monitoring of the quantity and quality of AMs used by farmers and the AM residues in the farmed species and in the environment, and promote measures to reduce potential human health risks associated with AMR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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