A closer look at the antibiotic‐resistant bacterial community found in urban wastewater treatment systems
Bibliographic record
Abstract
The conventional biological treatment process can provide a favorable environment for the maintenance and dissemination of antibiotic-resistant bacteria and the antibiotic resistance genes (ARG) they carry. This study investigated the occurrence of antibiotic resistance in three wastewater treatment plants (WWTP) to determine the role they play in the dissemination of ARGs. Bacterial isolates resistant to tetracycline were collected, and tested against eight antibiotics to determine their resistance profiles and the prevalence of multiple antibiotic resistance. It was found that bacteria resistant to tetracycline were more likely to display resistance to multiple antibiotics compared to those isolates that were not tetracycline resistant. Polymerase chain reaction (PCR) was used to identify the tetracycline resistance determinants present within the bacterial communities of the WWTPs and receiving waters, and it was found that ARGs may not be released from the treatment process. Identification of isolates showed that there was a large diversity of species in both the tetracycline-resistant and tetracycline-sensitive populations and that the two groups were significantly different in composition. Antibiotic resistance profiles of each population showed that a large diversity of resistance patterns existed within genera suggesting that transmission of ARG may progress by both horizontal gene and vertical proliferation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".