Pharmaceuticals in the marine environment: a review
Bibliographic record
Abstract
Despite the increasing presence of pharmaceuticals in marine environments and their potential negative impacts, little research has been reported on the level and occurrence of these contaminants in the marine ecosystem. This review provides information on the occurrence (level–concentration) of pharmaceuticals in marine environments including seawater, sediments, and organisms within and (or) around this ecosystem. Also, the classification, sources, metabolism, and fate of these contaminants in the marine environment were discussed to identify knowledge gaps. We showed that antibiotics are the most commonly investigated and detected drugs in marine environments. In addition, this review suggested that focused case studies should be a priority for future research and highlighted the need for future assessments of the potential risks of pharmaceuticals to marine species. We also suggested that it is necessary to monitor the level of the most frequent and widespread pharmaceuticals like antibiotics and nonsteroidal anti-inflammatory drugs in sewage and marine outfalls. Finally, we concluded that there is a need for the development of effective treatment methods for the removal of these pollutants from wastewater before their discharge into the receiving marine environment or the main drinking water networks.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".