Metal bioaccumulation and oxidative stress in yellow perch (<i>Perca flavescens</i>) collected from eight lakes along a metal contamination gradient (Cd, Cu, Zn, Ni)
Bibliographic record
Abstract
Links among metal exposure, metal accumulation, and metal-induced effects were explored in indigenous yellow perch (Perca flavescens) collected from eight lakes located along a metal concentration gradient in two mining regions. Fish exposure to Cd, Cu, Ni, and Zn was estimated on the basis of calculated free metal ion concentrations in lake waters. Hepatic metal concentrations were determined and various markers of oxidative stress were measured to assess metal effects in liver cells. Accumulated metals were not consistently related to ambient free metal ion concentrations, possibly because of unaccounted for fluctuations in lake water metal concentrations. Accumulated metals were associated with limited oxidative stress in perch livers, as evidenced by reductions in glutathione concentrations and glutathione reductase activities. However, this stress appeared to be minor, since increasing hepatic Cu concentrations were associated with reduced lipid peroxidation, a response opposite to that predicted from basic principles. Our results suggest that oxidative stress will not have direct repercussions on the health of the perch at the individual level. We speculate that the observed increase in metallothionein concentrations with increasing accumulated metals might afford protection against reactive oxygen species.
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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) | 0.000 | 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".