Managing aquatic parasites for reduced drug resistance: lessons from the land
Bibliographic record
Abstract
Atlantic salmon farming is one of the largest aquaculture industries in the world. A major problem in salmon farms is the sea louse ectoparasite Lepeophtheirus salmonis, which can cause stress, secondary infection and sometimes mortality in the salmon host. Sea lice have substantial impacts on farm economics and potentially nearby wild salmonid populations. The most common method of controlling sea louse infestations is application of chemicals. However, most farming regions worldwide have observed resistance to the small set of treatment chemicals that are available. Despite this, there has been little investigation of treatment strategies for managing resistance in aquaculture. In this article, we compare four archetypical treatment strategies inspired by agriculture, where the topic has a rich history of study, and add a fifth strategy common in aquaculture. We use an agent-based model (ABM) to simulate these strategies and their varying applications of chemicals over time and space. We analyse the ABM output to compare how the strategies perform in controlling louse abundance, number of treatments required and levels of resistance in the sea louse population. Our results indicated that among the approaches considered applying chemicals in combination was the most effective over the long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".