Environmental exposure of freshwater mussels to contaminants of emerging concern: Implications for species conservation
Bibliographic record
Abstract
Contaminants of emerging concern (CECs) are prevalent in aquatic landscapes and may be a factor in population declines of aquatic and terrestrial fauna. Yet, there are limited data to assess the impacts of CECs to species. Understanding CEC impacts is particularly important for imperiled freshwater mussels which provide valuable ecosystem services. CEC exposure of freshwater mussels was characterized by evaluating sites with and without the federally endangered mussel (Villosa fabalis) in three subwatersheds of the Maumee River, Ohio, USA, a tributary of the Great Lakes Basin. Analyses of water, sediment, and tissue concentrations of two common mussels (Eurynia dilatata and Lampsilis cardium) indicated different CEC exposures across all 6 sites. Distinct CEC signatures were found across the three media types suggesting as mussels interact with water and sediment they may be experiencing different exposure concentrations and mixtures of CECs at different life stages. Of the 83 CECs which were detected, agricultural CECs dominated sediments, pharmaceuticals were common in tissues and water, and 16 of the 83 CECs were found co-occurring in mussel tissue, water, and sediment. There were no species differences in the CEC signatures indicating all mussels, including species of concern, may be experiencing similar exposure. Comparisons to known CEC standards indicate some exceedances in the Maumee watershed including locations of federally listed mussel species. This study provides evidence of the complexity of CEC mixes in a Great Lakes watershed and the need to understand how CECs impact declining aquatic fauna.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".