Effects of environmental metal contamination on the condition, swimming performance, and tissue metabolic capacities of wild yellow perch (<i>Perca flavescens</i>)
Bibliographic record
Abstract
In this study, the general condition, swimming capacities, and tissue metabolic capacities and metal concentrations in wild yellow perch (Perca flavescens) were examined from a range of metal-contaminated lakes near Sudbury, Ontario. Fish exposed to elevated environmental cadmium and copper concentrations showed higher liver concentrations of these metals and lower condition indices. Because growth rate appeared lower in the most metal-contaminated fish, the high tissue activities of nucleoside diphosphate kinase, an indicator of biosynthesis, in these fish indicated an increased rate of protein turnover and suggested a bioenergetic cost of metal exposure. Yellow perch from the most metal-contaminated lakes exhibited lower aerobic capacities, as indicated by citrate synthase and β-hydroxyacyl coenzyme A dehydrogenase activities, two mitochondrial enzymes involved in aerobic adenosine triphosphate production, and by critical swimming speed. There was no evidence from our data that environmental metal exposure affected anaerobic capacities of tissues as measured by lactate dehydrogenase activities or anaerobic fast-start swimming performance. Overall, these data show that metal exposure leads to measurable effects on metabolic capacities in wild yellow perch.
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 | 0.001 |
| 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".