Use of lumpfish for sea‐lice control in salmon farming: challenges and opportunities
Bibliographic record
Abstract
Abstract Efficient sea‐lice control remains one of the most important challenges for the salmon farming industry. The use of wrasse (Labridae) as cleaner fish offers an alternative to medicines for sea‐lice control, but wrasse tend to become inactive in winter. Lumpfish (Cyclopterus lumpus) continue to feed on sea‐lice at low temperatures, and commercial production has escalated from thousands of fish in 2010 to well over 30 million juveniles deployed in 2016. However, production still relies on the capture of wild broodstock, which may not be sustainable. To meet global industry needs, lumpfish production needs to increase to reach c. 50 million fish annually and this can only come from aquaculture. We review current production methods and the use of lumpfish in sea cages and identify some of the main challenges and bottlenecks facing lumpfish intensification. Our gap analysis indicates that the areas in most need of research include better control of maturation for year‐round production; formulation of appropriate diets; artificial selection of elite lines with desirable traits; and development of vaccines for certified, disease‐free juvenile production. The welfare of farmed lumpfish also needs to be better quantified, and more information is needed on optimal densities and tank design. Finally, the risk of farmed lumpfish escaping from net pens needs to be critically assessed, and we argue that it might be beneficial to recover cleaner fish from salmon cages after the production cycle, perhaps using them as broodstock, for export to the Asian food markets or for the production of animal feeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".