Science response: sea lice on Atlantic salmon farms and wild Pacific salmon in British Columbia
Bibliographic record
Abstract
Fisheries and Oceans Canada (DFO), under the Sustainable Aquaculture Program, is committed to deliver science-based decision making related to sustainable aquaculture activities. In British Columbia (BC), DFO Aquaculture Management Division (AMD) is the regulatory body for managing aquaculture. Under the authority of the Fisheries Act and the Pacific Aquaculture Regulations, DFO issues marine finfish aquaculture licences that authorizes the licence holder to carry out aquaculture activities under prescribed conditions. Sea lice management is one of the finfish conditions of licence with prescribed monitoring windows and frequencies, regulatory response thresholds, and reporting requirements (DFO, 2022b). AMD requested that DFO Aquaculture Science provide science advice to inform the development and application of adaptive management approaches to address interactions between sea lice infestations on farmed Atlantic Salmon (Salmo salar) and wild Pacific Salmon populations in BC. While there are several species of sea lice, the focus of this request for advice was Lepeophtheirus salmonis. This science advice is expected to inform DFO’s management of sea lice on Atlantic Salmon farms. This science advice will address the following objectives: 1. Estimate the number of Lepeophtheirus salmonis copepodids (infective sea lice larval stage) produced by Atlantic Salmon farms under current farm management practices; 2. Summarize counts of Lepeophtheirus salmonis on wild juvenile Pacific Salmon in BC; and 3. Determine the statistical strength of association between sea lice infestation pressure on Atlantic Salmon farms and sea lice prevalence on wild juvenile Pacific Salmon populations in BC. This Science Response Report results from the National Peer Review process on the Association between sea lice from Atlantic Salmon farms and sea lice infestation on juvenile wild Pacific Salmon in British Columbia held on June 24, 2022.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".