Effects of pollution and parasites on biomarkers of fish health in spottail shiners <i>Notropis hudsonius</i> (Clinton)
Bibliographic record
Abstract
Seven biomarkers in 204 spottail shiners Notropis hudsonius were examined for effects of pollution and parasites on fish health at localities along the St Lawrence River, Canada. The number of pigmented macrophage centres and pigmented macrophages in the spleen was significantly higher at polluted localities receiving urban and industrial effluents than at reference localities, indicating that they were good indicators of exposure to pollution in spottail shiners. Seven of the nine species of parasites found in 1+ year fish showed significant correlations with biomarkers. More parasites (18 species) but fewer correlations with biomarkers were observed in 2+ year fish, indicating that parasite effects were more pronounced in young spottail shiners. A significant negative relationship was observed between condition factor and Neoechinorhynchus rutili in 1+ year fish, suggesting its potential pathological significance in young spottail shiners. High abundance of Plagioporus sinitsini was associated with higher spleen macrophage counts and lower indices of condition at polluted localities. Furthermore, infection by P. sinitsini in polluted conditions appeared to have a greater negative effect on fish health than either stressor alone, providing further evidence that parasites should be considered when examining effects of pollution on fish health.
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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 | 0.000 | 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".