Sea lice population and sex differences in P‐glycoprotein expression and emamectin benzoate resistance on salmon farms in the Bay of Fundy, New Brunswick, Canada
Bibliographic record
Abstract
BACKGROUND: Parasitic sea lice are a major challenge for salmon aquaculture. This is especially due to the recent development of resistance to emamectin benzoate (EMB) in the parasite. We investigated: (1) whether EMB treatment success in Grand Manan, Bay of Fundy, NB, Canada can be explained through EMB bioassay and P-glycoprotein (P-gp) mRNA expression studies; (2) if other populations of sea lice not under EMB selective pressure possess similar EMB sensitivity as Grand Manan sea lice populations; and (3) the heritability of EMB resistance in Lepeophtheirus salmonis. RESULTS: EMB bioassay results indicated population, species, sex and temporal differences in EMB EC50 values. RT-qPCR analyses revealed population and sex differences in P-gp mRNA levels, correlating with the bioassay results. Laboratory-reared sea lice maintained their EMB sensitivity status up to the F3 generation. Caligus elongatus, collected from Grand Manan showed more than twofold lower EMB EC50 values compared with L. salmonis collected from the same site. Concurrent exposure to EMB and verapamil yielded no increase in C. elongatus sensitivity to the parasiticide. CONCLUSION: Sea lice bioassay and P-gp mRNA studies can be used to track EMB resistance and sex differences in EMB sensitivity and P-gp mRNA levels exist in the parasite.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".