Bioaccumulation and trophic dilution of human pharmaceuticals across trophic positions of an effluent-dependent wadeable stream
Bibliographic record
Abstract
Though pharmaceuticals are increasingly observed in a variety of organisms from coastal and inland aquatic systems, trophic transfer of pharmaceuticals in aquatic food webs have not been reported. In this study, bioaccumulation of select pharmaceuticals was investigated in a lower order effluent-dependent stream in central Texas, USA, using isotope dilution liquid chromatography-tandem mass spectrometry (MS). A fish plasma model, initially developed from laboratory studies, was tested to examine observed versus predicted internal dose of select pharmaceuticals. Pharmaceuticals accumulated to higher concentrations in invertebrates relative to fish; elevated concentrations of the antidepressant sertraline and its primary metabolite desmethylsertraline were observed in the Asian clam, Corbicula fluminea, and two unionid mussel species. Trophic positions were determined from stable isotopes (δ(15)N and δ(13)C) collected by isotope ratio-MS; a Bayesian mixing model was then used to estimate diet contributions towards top fish predators. Because diphenhydramine and carbamazepine were the only target compounds detected in all species examined, trophic magnification factors (TMFs) were derived to evaluate potential trophic transfer of both compounds. TMFs for diphenhydramine (0.38) and carbamazepine (1.17) indicated neither compound experienced trophic magnification, which suggests that inhalational and not dietary exposure represented the primary route of uptake by fish in this effluent-dependent stream.
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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) | 0.000 | 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".