Cessation of a salmon decline with control of parasites
Bibliographic record
Abstract
The resilience of coastal social-ecological systems may depend on adaptive responses to aquaculture disease outbreaks that can threaten wild and farm fish. A nine-year study of parasitic sea lice (Lepeophtheirus salmonis) and pink salmon (Oncorhynchus gorbuscha) from Pacific Canada indicates that adaptive changes in parasite management on salmon farms have yielded positive conservation outcomes. After four years of sea lice epizootics and wild salmon population decline, parasiticide application on salmon farms was adapted to the timing of wild salmon migrations. Winter treatment of farm fish with parasiticides, prior to the out-migration of wild juvenile salmon, has reduced epizootics of wild salmon without significantly increasing the annual number of treatments. Levels of parasites on wild juvenile salmon significantly influence the growth rate of affected salmon populations, suggesting that these changes in management have had positive outcomes for wild salmon populations. These adaptive changes have not occurred through formal adaptive management, but rather, through multi-stakeholder processes arising from a contentious scientific and public debate. Despite the apparent success of parasite control on salmon farms in the study region, there remain concerns about the long-term sustainability of this approach because of the unknown ecological effects of parasticides and the potential for parasite resistance to chemical treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".