Salmon mass mortality events and occupational health and safety in Chilean aquaculture
Bibliographic record
Abstract
Mass mortality events (MMEs) threaten the health of fish and are also a potential threat to the health and safety of workers.This paper presents findings from a desktop risk assessment exercise focused on potential aquaculture occupational health and safety (AOHS) hazards and risks associated with MMEs in Chile.The study reviews academic and grey literature, government regulations and MME reports and statistics to assess the scale and distribution of MMEs; identifies associated documented and potential health and safety hazards; and documents and assesses policy responses to MMEs in the Chilean context through the lens of health and safety.The paper documents the size and regional distribution of salmon MME occurrences in Chile from 2016 to 2022.It discusses AOHS hazards associated with MMEs such as exposure to hydrogen sulfide, drowning and diving-related illnesses and potential issues around accessing hospitals and hyperbaric chambers for workers in remote aquaculture regions, as well as exposure to antibiotics and antibiotic residues.Recent Chilean regulatory requirements around reporting and management of MMEs that have the potential to help reduce identified MME-related risks for workers are described and addressed. Key policy highlights• Risk of mass mortality events (MMEs) in marine aquaculture is increasing as the industry expands and climate change increases fluctuations in marine conditions.Policies need to be developed to counteract these risks.• MMEs-related worker accidents and environmental degradation point to the urgent need to develop environmental and occupational health and safety (OHS) guidelines that prevent MMEs and protect workers and the environment.• The Chilean OHS guidelines related to aquaculture MMEs may be adapted and used in other countries.• Surveillance programs on harmful algal blooms and antibiotic resistance (AMR) markers may be a preventive measure towards MMEs and exposure to AMR markers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".