Selective Uptake and Bioaccumulation of Antidepressants in Fish from Effluent-Impacted Niagara River
Bibliographic record
Abstract
The continuous release of pharmaceuticals and personal care products (PPCPs) into freshwater systems impacts the health of aquatic organisms. This study evaluates the concentrations and bioaccumulation of PPCPs and the selective uptake of antidepressants in fish from the Niagara River, which connects two of the North American Great lakes (Erie and Ontario). The Niagara River receives PPCPs from different wastewater treatment plants (WWTPs) situated along the river and Lake Erie. Of the 22 targeted PPCPs, 11 were found at part-per-billion levels in WWTP effluents and at part-per-trillion levels in river water samples. The major pollutants observed were the antidepressants (citalopram, paroxetine, sertraline, venlafaxine, and bupropion, and their metabolites norfluoxetine and norsertraline) and the antihistamine diphenhydramine. These PPCPs accumulate in various fish organs, with norsertraline exhibiting the highest bioaccumulation factor (up to about 3000) in the liver of rudd (Scardinius erythrophthalmus), which is an invasive species to the Great Lakes. The antidepressants were selectively taken up by various fish species at different trophic levels, and were further metabolized once inside the organism. The highest bioaccumulation was found in the brain, followed by liver, muscle, and gonads, and can be attributed to direct exposure to WWTP effluent.
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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 | 0.001 | 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".