P-365 Promoting sentinel surveillance programs for antimicrobial resistance in Canadian salmon aquaculture, a possible and understated occupational health hazard
Bibliographic record
Abstract
<h3>Introduction</h3> Canadian salmon aquaculture is a high-risk industry with injury rates that surpass provincial averages in Atlantic Canada, yet few publications address occupational health hazards. Antimicrobial resistance (AMR) emergence is a growing public health concern, and the marine aquatic environment with its rich microbiota is particularly vulnerable to selection of AMR. Antibiotic use in the industry and other anthropogenic activities that result in the deposit of pollutants contaminated with antibiotics into the marine environment can together amplify selective pressure propagating AMR. Similar to terrestrial animal production facilities, there is concern for the development of hotspots for occupational exposures to AMR among aquaculture workers. As the fastest growing food production network globally, the aquaculture industry has been appealed by the Food and Agriculture Organization, among others, to standardize monitoring and to generate an evidence base to better understand the aetiology of AMR emergence in aquaculture settings. <h3>Objectives</h3> This case study will identify Canadian policies and practices that promote antimicrobial stewardship and surveillance in the aquaculture industry. Methods i) Compare antibiotic use by Canadian salmon aquaculture to other global industry leaders. ii) Compare regulatory regimes and surveillance strategies across industry leaders. <h3>Results</h3> Prescribed antibiotic use in Canadian salmon aquaculture exceeds that of Norway, the industry leader, which has implemented an array of strategies to drastically reduce antibiotic use since the 1990s. Unlike Norway, Canadian aquaculture lacks monitoring programmes for AMR and, furthermore, has yet to document possible occupational exposure pathways to this hazard. Current data repositories do not elucidate health risks associated with AMR emergence in aquaculture settings. <h3>Conclusion</h3> Canadian salmon aquaculture has an opportunity to lead the country’s animal production industries in the development of a standardized sentinel surveillance network to accommodate formal risk analyses and early warning systems. Continuous AMR surveillance coordinated with current public health monitoring would promote health protective strategy development and antimicrobial stewardship within the country.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".