Metabolic Costs of Exposure to Wastewater Effluent Lead to Compensatory Adjustments in Respiratory Physiology in Bluegill Sunfish
Bibliographic record
Abstract
Municipal wastewater effluent is a major source of aquatic pollution and has potential to impact cellular energy metabolism. However, it is poorly understood whether wastewater exposure impacts whole-animal metabolism and whether this can be accommodated with adjustments in respiratory physiology. We caged bluegill sunfish ( Lepomis macrochirus ) for 21 days at two sites downstream (either 50 or 830 m) from a wastewater treatment plant (WWTP). Survival was reduced in fish caged at both downstream sites compared to an uncontaminated reference site. Standard rates of O 2 consumption increased in fish at contaminated sites, reflecting a metabolic cost of wastewater exposure. Several physiological adjustments accompanied this metabolic cost, including an expansion of the gill surface area available for gas exchange (reduced interlamellar cell mass), a decreased blood-O 2 affinity (which likely facilitates O 2 unloading at respiring tissues), increased respiratory capacities for oxidative phosphorylation in isolated liver mitochondria (supported by increased succinate dehydrogenase, but not citrate synthase, activity), and decreased mitochondrial emission of reactive oxygen species (ROS). We conclude that exposure to wastewater effluent invokes a metabolic cost that leads to compensatory respiratory improvements in O 2 uptake, delivery, and utilization.
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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) | 0.001 | 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".