Monitoring and potential control of sea lice using an LED-based light trap
Bibliographic record
Abstract
Sea lice are ectoparasitic copepods that threaten salmon farming aquaculture and the viability of wild salmon populations. To control infestations on farmed salmon, several chemotherapeutants have been developed, but these are invasive (often causing fish stress and loss in production), costly, may induce parasite resistance over time, and their impact on the environment is a major social concern. Here, we show that a light-emitting diode (LED)-based light trap can be used to monitor sea lice presence on fish and in the water. The performance of the light trap was tested in experimental tanks and in the ocean. Plankton net tows were also performed to compare catches with those from light traps. The light trap caught ~70% of salmon lice larval stages loaded onto a tank and ~24% of the adults. It also acted as a delousing agent by removing ~8% of adult salmon lice infective on Chinook salmon ( Oncorhynchus tshawytscha ) smolts in tank experiments. In the ocean, the light trap caught 21 sea lice (10 Lepeophtheirus salmonis and 11 Caligus clemensi ), comprising free-swimming and attached stages, while plankton net tows failed to capture any. We conclude that light traps constitute an effective, noninvasive, environmentally friendly method to monitor 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".