Addressing the welfare needs of farmed lumpfish: Knowledge gaps, challenges and solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lumpfish ( Cyclopterus lumpus L.) are increasingly being used as cleaner fish to control parasitic sea lice, one of the most important threats to salmon farming. However, lumpfish cannot survive feeding solely on sea lice, and their mortality in salmon net‐pens can be high, which has welfare, ethical and economic implications. The industry is under increasing pressure to improve the welfare of lumpfish, but little guidance exists on how this can be achieved. We undertook a knowledge gap and prioritisation exercise using a Delphi approach with participants from the fish farming sector, animal welfare, academia and regulators to assess consensus on the main challenges and potential solutions for improving lumpfish welfare. Consensus among participants on the utility of 5 behavioural and 12 physical welfare indicators was high (87–89%), reliable (Cronbach's alpha = 0.79, 95CI = 0.69–0.92) and independent of participant background. Participants highlighted fin erosion and body damage as the most useful and practical operational welfare indicators, and blood parameters and behavioural indicators as the least practical. Species profiling revealed profound differences between Atlantic salmon and lumpfish in relation to behaviour, habitat preferences, nutritional needs and response to stress, suggesting that applying a common set of welfare standards to both species cohabiting in salmon net‐pens may not work well for lumpfish. Our study offers 16 practical solutions for improving the welfare of lumpfish and illustrates the merits of the Delphi approach for achieving consensus among stakeholders on welfare needs, targeting research where is most needed and generating workable solutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it