Assessment of Pharmaceuticals in Danube River: An analysis of NORMAN database
Bibliographic record
Abstract
Abstract In recent years, various anthropogenic factors have been contributing to the deterioration of water quality in the world’s major rivers due to the discharge of untreated or improperly treated municipal wastewater, industrial effluents, and agriculture runoffs. The presence of pharmaceuticals in surface water bodies, such as in river water, is increasingly becoming an environmental concern because of their toxicological consequences on the ecosystem. Pharmaceutical contamination of river water affects every trophic level of the aquatic biota along the down streams. This review aims to analyze the presence of pharmaceuticals in the Danube, the second longest river in Europe, connecting ten countries, running through their territories or being a border. Available data on contaminants of emerging concern (CECs) in the NORMAN database is used to conduct this study. Herein, temporal analysis of the distribution of CECs is presented. Further, the occurrence patterns of CECs in different countries along the Danube River basin are compared and evaluated. The results indicate that Austria, Germany, and Romania have controlled the overall pharmaceutical contamination in the river, while for Serbia and Slovakia, precautionary measures are needed. Moreover, findings from this research may be used to assess the health and environmental hazards associated with the presence of CECs in the Danube River basin. This study can assist in framing future policies to prevent adverse impacts on public health.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".