Discharge of pharmaceuticals into municipal sewers from hospitals and long-term care facilities
Bibliographic record
Abstract
The presence of pharmaceutically active compounds in the aquatic environment has become well established, and their presence is of potential concern because they are designed to produce biological response in the target receptor, may bear intrinsic toxicity (e.g. cytostatic agents, antibiotics) and they possess the potential to foster and maintain drug resistance. For both risk assessment and risk management purposes, it is important to identify the major sources of pharmaceuticals in the environment. Healthcare facilities may be major sources of the discharges of these compounds into municipal sewers. In this study, we investigated the contributions to the wastewater treatment plant (WWTP) influents from two hospitals and two long-term care homes of nine compounds. Twenty-four hour composite samples were collected over 5 consecutive days from the effluents of these facilities. The WWTPs receiving sewage from these facilities were also sampled on the same days to facilitate mass balance calculations. The results showed that the healthcare facilities contributed a greater proportion of the antibiotic compounds to the WWTPs than the other target compounds; with maximum contributions of ciprofloxacin by hospitals and long-term care homes of 26.7 and 37%, respectively.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".