Effects of Pharmaceutically Active Compounds on a Mixed Microbial Community Originating from a Municipal Wastewater Treatment Plant
Bibliographic record
Abstract
The growth and composition of microorganisms found in a municipal wastewater treatment plant were investigated in the presence of four pharmaceutically active compounds (PhACs) [ketoprofen, naproxen, carbamazepine, and clofibric acid] in batch reactors at varying organic loadings. Overall, the data suggest that microbial growth inhibition was correlated to organic loading rather than PhAC concentration. Significant inhibition ranging from 34 to 43% was observed under the lowest organic loading for all PhACs other than clofibric acid. No inhibition was observed at the highest organic loading. Higher microbial inhibition was not observed with increased PhAC concentration for a given organic loading. These results indicatethatthe presence of PhAC may affect microbial growth especially under lower organic loading conditions. Further validation is required with additional PhACs, organic substrates, and a wider loading range. In addition, significant microbial shifts were observed in the presence of ketoprofen and naproxen. These data suggest that, in addition to their effect on overall microbial growth, PhACs may affect the microbial ecology and additional research should be carried out to identify PhACs that have the potential of affecting ecologically important microorganisms in wastewater treatment processes and aquatic environments in general.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".