Lingering Effects of Legacy Industrial Pollution on Yellow Perch of the Detroit River
Bibliographic record
Abstract
We used yellow perch (Perca flavescens) captured at four sites differing in legacy industrial pollution in the Lake St. Clair-Detroit River system to evaluate the lingering sublethal effects of industrial pollution. We emphasized bioindicators of direct (toxicity) and indirect (chronic stress, impoverished food web) effects on somatic and organ-specific growth (brain, gut, liver, heart ventricle, gonad). Our results show that higher sediment levels of industrial contaminants at the most downstream Detroit River site (Trenton Channel) are associated with increased perch liver detoxification activity and liver size, reduced brain size, and reduced scale cortisol content. Trenton Channel also displayed food web disruption, where adult perch occupied lower trophic positions than forage fish. Somatic growth and relative gut size were lower in perch sampled at the reference site in Lake St. Clair (Mitchell's Bay), possibly because of increased competition for resources. Models used to determine the factors contributing to site differences in organ growth suggest that the lingering effects of industrial pollution are best explained by trophic disruption. Thus, bioindicators of fish trophic ecology may prove advantageous to assess the health of aquatic ecosystems. Environ Toxicol Chem 2023;42:2158-2170. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.000 | 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) | 0.000 | 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".