Sea-lice regulation in salmon-farming countries: how science shape policies for protecting wild salmon
Bibliographic record
Abstract
Abstract The proliferation of sea lice from aquaculture may substantially aggravate the decline in marine survival of wild salmons. In some countries, this risk has motivated regulators to adopt more precautionary policies; in other countries, however, regulators have disputed the need for stricter regulation. This article compares the sea-lice regulations of Norway, Scotland, Ireland, and Canada (British Columbia), showing how varying interpretations of the science on farm–wild interactions have shaped efforts to scale up regulatory measures for mitigating health hazards and mortality risks for wild salmon. In Norway and Scotland, scientific consensus has expedited cooperation between research and governing institutions and facilitated ambitious policy reforms. In Ireland and Canada, by contrast, scientific controversy around the scale of farm-lice impacts on wild salmon populations has led to conflict and disagreement between researchers and policymakers, and to failure of reform attempts desired by wild salmon stakeholders.
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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 | 0.054 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".