Effects of tetracycline and ibuprofen on the relative abundance of microbial eukaryotic and bacterial populations in wastewater treatment
Bibliographic record
Abstract
The activated sludge process in a wastewater treatment plant (WWTP) relies on the activity of microbes to reduce the organic and inorganic matter and produce effluent that is safe to discharge into receiving waters. This research examined the effects of the non-steroidal anti-inflammatory drug ibuprofen and the antibiotic tetracycline on the relative abundance and composition of eukaryotes and bacteria in the microbial population present in activated sludge from a WWTP. The current investigation was designed to observe the impact of these contaminants, at low (environmentally relevant concentrations) as well as high concentrations of the drugs. Using 16S and 18S rRNA gene primer sets and quantitative polymerase chain reaction, the abundance of each population was monitored as well as the relative ratio of the two populations under the various conditions. It was found that current environmentally relevant concentrations of ibuprofen (100 ng/mL) stimulated eukaryotic growth but higher concentrations (2,000 ng/mL, 100,000 ng/mL) reduced their numbers significantly especially in the presence of tetracycline. Finally using denaturing gradient gel electrophoresis, some of the more abundant eukaryotes were identified and it was noted that high ibuprofen and tetracycline concentrations favoured the abundance of some genera.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".