Seasonal and Regional Variations of Metal Contamination and Condition Indicators in Yellow Perch ( <i>Perca flavescens</i> ) along Two Polymetallic Gradients. I. Factors Influencing Tissue Metal Concentrations
Bibliographic record
Abstract
This study examined relationships among water, sediment, diet, and fish tissue metal (Cd, Cu, Ni, Se, and Zn) concentrations in yellow perch from metal gradients in two regions (Sudbury (S), Ontario, and Rouyn-Noranda (RN), Québec, Canada) in two seasons (spring and summer). The objectives of this study were (1) to examine the influences of aqueous and dietary metal contamination on yellow perch liver and kidney metal accumulation; (2) to compare the seasonal and regional variations in gut content and tissue metal concentrations along the two gradients studied; and (3) to investigate the potential of metals for tissue accumulation under conditions of life-long chronic exposure. Our results suggest a greater aqueous than dietary influence on tissue metal concentrations for all metals examined except Cd, where the opposite was observed. Metals did not accumulate in older fish, except for Cd that accumulated with age in RN, but not S, fish. Regional, but also metal-specific differences in metal handling capacities are proposed. Fish from neither region appeared capable of regulating tissue Cd concentrations, but fish from both regions regulated Zn tightly. Sudbury fish appeared better at regulating tissue Cu, Ni, and perhaps also Se concentrations than RN fish, suggesting acclimation or selection for metal tolerance. There were several significant seasonal effects on tissue metal concentrations. However, close examination of the dataset does not allow proposing the presence of a season-linked mechanism explaining these variations, precluding a modeling approach and implying that repeat sampling within and among years is required for proper ecological risk assessment.
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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".