A data compilation of antibiotic treatments in Canadian finfish aquaculture from 2016 to 2021 and the cumulative usage of antibiotics and antiparasitic drugs at marine sites
Bibliographic record
Abstract
Antibiotics can be introduced from aquaculture facilities into marine sediments, where they may affect benthic communities and generate antimicrobial resistance (AMR). Antibiotics can be used in tandem with antiparasitics, and the effects of their combined usage could affect AMR patterns and transmit antibiotic resistance genes. This study compiles patterns of antibiotic and antiparasitic drug usage from 2016 to 2021 in British Columbia, New Brunswick, Newfoundland and Labrador, and Nova Scotia (Canada) and compares them internationally. There is an overall reduction in rates of antibiotic use per fish produced in Canadian aquaculture sites from 2016 to 2021. Compared to other salmon-producing countries, Canadian antibiotic rates per tonne of fish are lower than in Chile, but higher, on average, than in Norway and Scotland. Florfenicol (FLO) and oxytetracycline (OTC) are the prevalent antibiotics used in most salmon-producing countries, including Canada. We also note that <30% of sites used two drugs per year and <18% of sites used three drugs, with most of these sites using one antibiotic and one antiparasitic. There is an increase in FLO and a decrease use in OTC use in Canada, which could be positive environmentally considering FLO's lower dose and environmental persistence. The east and west coasts show comparable antibiotic rates, with an average rate of 78 mg/kg in BC and 76 mg/kg in the Atlantic provinces; however, they have differences in the type of antibiotic (average use of OTC: BC 38 mg/kg vs. Atlantic 214 mg/kg; average use of FLO: BC 40 mg/kg vs. Atlantic 10 mg/kg). Other distinctions between coasts include variation in the timing of treatments, with seasonal use in the Atlantic provinces (May–November) and year-round use in BC. This pattern is likely influenced by differences in water temperature and, subsequently, potential susceptibility to aquatic diseases. Disease prevalence and veterinarian choice/preferred treatments are factors that, we hypothesize, may impact treatment choice; however, additional information is required to adequately comment on this point. This data compilation includes spatial descriptions that could be used for area prioritization in future studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".