Threshold-based disease treatment approach modulates economic, conservation and evolutionary trade-offs in sea louse-salmon aquaculture system
Bibliographic record
Abstract
Mitigating negative downstream impacts of parasitic disease in aquaculture settings entails tradeoffs: reducing parasite loads has economic and conservation benefits, but treatment is often expensive and frequent treatment can lead to resistance evolution. Options for mitigating these potential trade-offs depend on the management context. For example, in the sea louse-salmon system, managers use discrete treatment applications to control louse burdens, applying treatment when parasite burdens exceed a target threshold. To analyze the effect of a threshold-based control of disease treatment on economic, conservation, and evolutionary outcomes, we incorporate discrete treatment into a dynamical model of sea louse-salmon systems with disease spillover to wild populations. The model follows both salmon hosts and sea lice through domestic, wild, and migratory populations, with treatment occurring when sea lice exceed a target threshold. Our model shows that simultaneous economic and conservation win-wins are possible: there are treatment threshold choices that lead to relatively high wild juvenile salmon population sizes and relatively low economic losses, especially when treatment is very effective or treatment is cheap. However, positive evolutionary outcomes are harder to capture and occur most often when treatment efficacy is low and the treatment threshold is either near zero or very high. Expanding the management toolbox beyond choices of treatment threshold and treatment efficacy could help managers better capture positive economic, evolutionary and conservation outcomes in the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".