Disease resistance is related to inherent swimming performance in Atlantic salmon
Bibliographic record
Abstract
BACKGROUND: Like humans, fish can be classified according to their athletic performance. Sustained exercise training of fish can improve growth and physical capacity, and recent results have documented improved disease resistance in exercised Atlantic salmon. In this study we investigated the effects of inherent swimming performance and exercise training on disease resistance in Atlantic salmon.Atlantic salmon were first classified as either poor or good according to their swimming performance in a screening test and then exercise trained for 10 weeks using one of two constant-velocity or two interval-velocity training regimes for comparison against control trained fish (low speed continuously). Disease resistance was assessed by a viral disease challenge test (infectious pancreatic necrosis) and gene expression analyses of the host response in selected organs. RESULTS: An inherently good swimming performance was associated with improved disease resistance, as good swimmers showed significantly better survival compared to poor swimmers in the viral challenge test. Differences in mortalities between poor and good swimmers were correlated with cardiac mRNA expression of virus responsive genes reflecting the infection status. Although not significant, fish trained at constant-velocity showed a trend towards higher survival than fish trained at either short or long intervals. Finally, only constant training at high intensity had a significant positive effect on fish growth compared to control trained fish. CONCLUSIONS: This is the first evidence suggesting that inherent swimming performance is associated with disease resistance in fish.
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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 | 0.000 |
| 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".