Anaemia and salmonid swimming performance: the potential effects of sub‐lethal sea lice infection
Bibliographic record
Abstract
The effect of two known rates of repeated blood loss on rainbow trout Oncorhynchus mykiss swimming performance was measured and blood‐feeding rates of sea lice Lepeophtheirus salmonis were calculated to predict the point at which blood ingestion causes anaemia in infected fish. Known quantities of blood were sampled from rainbow trout over a 5 day period followed by critical swimming performance ( U crit ) testing. A predictive equation was developed using masses of blood‐feeding sea lice and host blood loss calculated for increasing levels of sea lice infection. Blood loss of 8% total blood volume caused a decrease in U crit for rainbow trout. Total blood volume losses of 3·2% reduced erythrocyte stores, but did not affect fish swimming performance. The predictive feeding rate model suggests that 15–25% of the tissue consumed by sea lice is blood. This consumption of blood at higher sub‐lethal infection levels (≥0·5 sea lice g −1 ) may cause anaemia and a further decrease in swimming performance. Anaemia would compound the osmotic balance problems due to infection and potentially precipitate the morbidity seen at lethal sea lice levels (0·75–1·0 lice g −1 ).
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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 | 0.000 | 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) | 0.000 | 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 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".