Personality profiling may help select better cleaner fish for sea-lice control in salmon farming
Bibliographic record
Abstract
Abstract Lumpfish ( Cyclopterus lumpus ) are increasingly being used as cleaner fish to control parasitic sea-lice in salmon farming, but cleaning rates are very variable and not all individuals eat sea-lice, which increases the risk of emaciation and has ethical and practical implications. Selecting good cleaners is a priority to make the industry more sustainable, but there is little information on what behaviours make cleaner fish effective under a commercial setting. We examined variation in lumpfish personalities according to the five-factor personality model that takes into account differences in activity, anxiety (shelter use, thigmotaxis), aggression, sociality, and boldness (neophobia). We then quantified how variation in lumpfish personalities influenced interactions with naïve Atlantic salmon ( Salmo salar ), without the confounding effects of variation in sea-lice loads. Variation in activity, sociality, aggression and neophobia, but not in anxiety, was repeatable, which is consistent with a heritable basis. Neophilic, non-aggressive lumpfish spent more time inspecting salmon than neophobic and aggressive individuals, but salmon fled in the presence of the most active and social individuals, suggesting there may be an optimal cleaner fish personality amenable to artificial selection. The personality screening protocols developed in this study could inform a more efficient use of cleaner fish in salmon farming and reduce the number of individuals required to control sea-lice.
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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 | 0.001 |
| 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 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".