Salmon lice infestations on sea trout predicts infestations on migrating salmon post-smolts
Bibliographic record
Abstract
Abstract Impacts of sea lice (Lepeophtheirus salmonis or Caligus spp.) on wild salmonids is currently one of the most important issues facing management of fish farms in salmon producing countries in the northern hemisphere. Surveillance of sea lice on wild Atlantic salmon (Salmo salar) is often hampered by the ability to catch enough migrating post-smolts. Therefore, sea lice abundance on anadromous trout (Salmo trutta) is often used to infer sea lice abundance on migrating salmon post-smolt. However, the assumption that there is a relationship between the abundance of lice on salmon and trout has never been tested. Here we use a dataset of sea lice on salmon post-smolt and sea trout that have been caught simultaneously in trawl hauls, to evaluate the correlation in abundance of sea lice between the two species using various statistical models. We demonstrate that trout generally has higher abundances of sea lice than salmon. Average lice per gram fish on sea trout (log transformed) predicted the abundance of lice on salmon best. Negative binomial models of lice counts were preferable to using trout lice counts as direct estimates of salmon lice abundance, and they had better predictive ability than logit models of high (vs. low) lice counts. Including the size of the salmon increased the predictive ability of the model, but these data are not generally available. The effect of salmon weight may have been a direct effect of body size, or an indirect effect of time spent in marine waters. Finally, we predict lower salmon lice counts on migrating salmon with our selected binomial model than with the current method of using trout lice counts as a direct estimator on salmon lice counts, and demonstrate that management advice would change considerably depending on the chosen method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".