Quantitative analysis of mass mortality events in salmon aquaculture shows increasing scale of fish loss events around the world
Bibliographic record
Abstract
Globally, salmon aquaculture promises to contribute to sustainable sources of animal protein for a growing human population. However, the growth of the industry also includes increased reports of mass mortality events-disaster events where large numbers of fish die in short periods of time. As salmon production increases in scale and more technology is used to grow salmon in contexts otherwise not suited for them, there is a possibility for more frequent and more severe mortality events. Despite investigations into specific cases of mass mortality events-no global study has been conducted to see if large scale mortality is increasing in frequency and scale. Using a global dataset of publicly available and government-collated data on salmon mortality events including nations responsible for the majority of salmon aquaculture, we document trends in mortality events, showing that in some of the major salmon producing nations of the world (in particular Norway, Canada, and the UK), mass mortality events have increased in frequency from 2012 to 2022. We also show that the scope of mass mortality events has increased over time-that is, the upper bound of how many fish were killed in a specific mortality event has increased over time. Finally, the expected maximum size of a mass mortality event differs from country to country, but is likely much larger than site and jurisdictional thresholds of concern for animal welfare, early warning thresholds, and capacity to respond to mortality events. The consequences of the increased scale and scope of mass mortality events extend past aquaculture production to include severe consequences to aquaculture companies and to coastal communities who depend on aquaculture. Our results agree with predictions of the concept of "manufactured risk", which suggests that risk emerges from the aggressive use of technology to optimize production in variable environments, and we argue that there is a need for more fine-scale and standard data collection on salmon mortality events, and that future investigations into salmon aquaculture should increase focus on disaster potential and realization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".