Dissemination of Antibiotic Resistance Genes into natural environments and Wastewater Treatment Plants - Is there a link?
Bibliographic record
Abstract
The misuse of antibiotics has led to the emergence and spread of antibiotic resistant bacteria (ARB), which is of great concern to public health. Normally, wastewaters containing ARB originated from humans and animals are processed in wastewater treatment plants (WWTPs). Although it has been demonstrated that most WWTPs effectively and efficiently remove harmful bacteria and antibiotics from sewage waters before their release into local natural environments, recent molecular-based studies have revealed the unexpected presence of high levels of clinically-relevant antibiotic resistance genes (ARGs) in treated waters. We are conducting a research of the literature to better understand the connections between operations in WWTPs and the dissemination of antibiotic resistance genes (ARGs) into natural environments. Taking cases from different parts of the world, we are analyzing reports describing ARGs originally implicated in hospital infections, such as those encoding for extended spectrum beta lactamases (ESBL), and their fate in locations surrounding water treatment facilities. Also, we are reviewing the current understanding regarding risk management to limit the potential dissemination of ARGs to natural ecosystems via water treatment facilities. Preliminary results of these analyses will be presented. * Indicates faculty mentor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".