Joint effects of parasitism and pollution on oxidative stress biomarkers in yellow perch Perca flavescens
Bibliographic record
Abstract
Yellow perch Perca flavescens were collected from a contaminated site and a reference site in the St. Lawrence River, Quebec, Canada. Fish were assessed for oxidative stress (lipid peroxidation and reduced glutathione levels) and parasitism by the nematode Raphidascaris acus and metacercariae of the digenean Apophallus brevis. Lipid peroxidation is not only considered a measure of oxidative stress, but of stress in general, and thus serves as an indicator of fish health. Fish from the contaminated site exhibited higher levels of lipid peroxidation than those from the reference site, independent of parasitic infections. However, fish infected with R. acus at the contaminated site tended to have higher levels of lipid peroxidation than uninfected fish at the same site, whereas no difference was observed between infected and uninfected fish at the reference site. Yellow perch infected with > 10 metacercariae of A. brevis expressed higher levels of lipid peroxidation than those infected with < 10 metacercariae at both the contaminated and the reference sites. No differences were found in levels of reduced glutathione in liver or muscle in relation to site or either parasite species. Results support the use of lipid peroxidation as a biomarker of water contamination. They further suggest that lipid peroxidation may be used as a biomarker of pathological effects caused by parasitism. Most importantly, results demonstrate that contaminants and parasites occurring together exacerbate oxidative stress in fish, suggesting that parasitized fish in polluted environments are in a poorer state of health than uninfected 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".