Effects of pharmaceutical mixtures in aquatic microcosms
Bibliographic record
Abstract
Pharmaceuticals have a wide range of biological properties and are released into the environment in relatively large amounts, yet little information is available regarding their effects or potential ecological risks. We exposed outdoor aquatic microcosms to combinations of ibuprofen (a nonsteroidal anti-inflammatory drug), fluoxetine (a selective serotonin reuptake inhibitor), and ciprofloxacin (a DNA gyrase-inhibiting antibiotic) at concentrations of 6, 10, and 10 microg/L, respectively (low treatment [LT]); 60, 100, and 100 microg/L, respectively (medium treatment [MT]); and 600, 1,000, and 1,000 microg/L, respectively (high treatment [HT]). We maintained these concentrations for 35 d. Few responses were observed in the LT; however, effects were observed in the MT and HT. Fish mortality occurred in the MT (<35 d) and in the HT (<4 d). Phytoplankton increased in abundance and decreased in diversity (number of taxa) in the HT, with consistent trends being observed in the MT and LT. Zooplankton also showed increased abundance and decreases in diversity in the HT, with consistent trends being observed in the MT. Multivariate analyses for zooplankton and phytoplankton suggested interactions between these communities. Lemna gibba and Myriophyllum spp. showed mortality in the HT; growth of L. gibba was also reduced in the MT. Bacterial abundance did not change in the HT. All responses were observed at concentrations well below the equivalent pharmacologically active concentrations in mammals. Although the present data do not suggest that ibuprofen, fluoxetine, and ciprofloxacin are individually causing adverse effects in surface-water environments, questions remain about additive responses from mixtures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".