Live Fast and Die Young: Metal Effects on Condition and Physiology of Wild Yellow Perch from along Two Metal Contamination Gradients
Bibliographic record
Abstract
ABSTRACT This review summarizes some of the main findings of our work with the Metals in the Environment Research Network examining seasonal and regional effects on metal accumulation, growth, condition, and physiology in wild yellow perch (Perca flavescens) from 10 lakes comprising two metal contamination gradients in the industrial regions of Sudbury, Ontario and Rouyn-Noranda, Québec, Canada. The specific objectives of this review are: (1) to propose threshold tissue metal concentrations to discriminate between fish from contaminated and reference sites; (2) to identify factors that can influence metal accumulation and fish condition; and (3) to define an experimental approach for measuring metal effects in wild yellow perch. Using tissue thresholds appeared useful not only for discriminating fish from clean or contaminated environments, but also provided a simple approach to examine metabolic consequences of tissue metal accumulation. Overall, fish from Sudbury grew faster, expressed higher aerobic capacities, and died younger, but also appeared better at limiting accumulation of some metals than Rouyn-Noranda fish. The condition of the latter fish was clearly more affected by metals than Sudbury fish. Finally, our dataset allows us to propose that yellow perch are highly suitable for ecological risk assessment studies of metal effects in wild fish, but that fish size, season, and region must be considered in sampling design and that several reference sites must be studied for meaningful conclusions to be reached.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".