A partly stage-structured model for the abundance of salmon lice in salmonid farms
Bibliographic record
Abstract
The parasitic salmon louse constrains growth in the Norwegian salmon farming industry through density dependent host-parasite interaction. Hence, there is a need for insight into how increases in salmon production, varying spatial organisation of the production and pest control strategies affect salmon louse population dynamics. Here we present a new salmon louse model for exploring effects of varying salmon farming conditions on spatio-temporal abundances of the parasite. The salmon louse model is partly stage-structured, comprising of (i) adult female lice and (ii) other mobile stages of lice. The abundance of adult females depend on survival of females from previous weeks and recruitment from the other mobile group of lice. The other mobiles also depend on survival of other mobiles from previous weeks, as well as recruitment from the previous generation of reproducing adult females from the same farm or from farms in the neighbourhood. In addition, expected abundances of the two stage-groups are modelled as functions of biological and physical covariates. The model is fitted to weekly salmon farm data covering all marine farms producing salmonids along the Norwegian cost over the years 2012-2016. Among novel results from fitting the model are estimates of the time-lag structure representing recruitment of other mobile lice from the previous generation adult females for different temperatures. Furthermore, the model estimates how various factors affect the susceptibility of fish on farms to louse infection and effects of treatments to control infection. Finally, the model estimates density dependent effects of increasing the number of fish in farms and of increasing the numbers of farms, on the rate of recruitment of other mobile lice. Analytically, the parameters representing density dependencies suggest that few farms with many fish and large between farm distances is effective in terms of minimising the overall output of salmon lice infection.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".