Physiological impact of sea lice on swimming performance of Atlantic salmon
Bibliographic record
Abstract
Atlantic salmon Salmo salar were infected with two levels of sea lice Lepeophtheirus salmonis (0·13 ± 0·02 and 0·02 ± 0·00 sea lice g−1). Once sea lice became adults, the ventral aorta of each fish was fitted with a Doppler cuff to measure cardiac output (Q̇), heart rate (fH) and stroke volume (VS) during swimming. Critical swimming speeds (Ucrit) of fish with higher sea lice numbers [2·1 ± 0·1 BL (body lengths) s−1] were significantly lower (P < 0·05) than fish with lower numbers (2·4 ± 0·1 BL s−1) and controls (sham infected, 2·6 ± 0·1 BL s−1). After swimming, chloride levels in fish with higher sea lice numbers (184·4 ± 11·3 mmol l−1) increased significantly (54%) from levels at rest and were significantly higher than fish with fewer lice (142·0 ± 3·7 mmol l−1) or control fish (159·5 ± 3·5 mmol l−1). The fH of fish with more lice was 9% slower than the other two groups at Ucrit. This decrease resulted in Q̇ not increasing from resting levels. Sublethal infection by sea lice compromised the overall fitness of Atlantic salmon. The level of sea lice infection used in the present study was lower than has previously been reported to be detrimental to wild Atlantic salmon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".