Sea lice treatments, management practices and sea lice sampling methods on Atlantic salmon farms in the Bay of Fundy, New Brunswick, Canada
Bibliographic record
Abstract
Proper monitoring of all stages of sea lice is imperative to ensure the strategic timing of treatments. Although sea lice receive close attention and management for production purposes, there are no regulations for the reporting of lice burdens on salmon farms in Atlantic Canada, nor are there officially standardized protocols for conducting sea lice counts in the field. The purpose of this study was to complete a survey of Atlantic salmon farms in the Bay of Fundy, NB, Canada. The survey addressed many aspects of sea lice monitoring and control, including methods for sea lice sampling at the site, types of treatments administered for control of sea lice and management practices and decisions regarding the control of sea lice. A total of 83 surveys were completed from July to December 2002. Twelve of the 60 respondents owned and/or operated more than one site and completed a survey for each of their sites at the time of the interview. The position of individuals surveyed included 48 site managers (representing 63 sites), three site owners (representing 11 sites), eight individuals who were both owner and manager (representing nine sites) and one site veterinarian (representing one site). Surveys were completed on 30 sites from the Limekiln Bay and surrounding areas, 23 sites from Grand Manan Island, 18 sites from Deer Island, six sites from Campobello Island and six sites from Passamaquoddy Bay. Fish farmers, in concert with their veterinarians, generally perform intensive monitoring and control practices. However, there is a continued reliance on emamectin benzoate (SLICE®) for sea lice control on Atlantic salmon farms in the Bay of Fundy, which raises concerns regarding the potential for sea lice to develop resistance to the drug.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".