Quantifying the influence of salinity and temperature on the population dynamics of a marine ectoparasite
Bibliographic record
Abstract
Sea lice are common ectoparasites of farmed and wild salmonids and can cause substantial morbidity and mortality in their hosts. While sea lice infections are common in estuarine areas with variable salinity, the effects of salinity on population dynamics are poorly understood. We used existing literature to parameterize salinity-dependent logistic mortality curves for different life stages of sea lice. We then used population matrix models to characterize the effects of temperature and salinity on sea louse population growth. Our models showed that low salinity decreases survival, while low temperature retards sea louse development. In contrast with the linear effects of temperature on sea louse development, salinity has a nonlinear effect on sea louse survival; values below 20 psu cause mortality, while values above 20 psu have little effect on survival. Simulations showed that sea louse population growth can be greatest in zones that are intermediate between estuarine and oceanic. In these cases population growth is not limited by the low salinities found in more estuarine sites or the low temperatures found in more oceanic sites.
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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".