From municipal sewage to drinking water: fate and removal of pharmaceutical residues in the aquatic environment in urban areas
Bibliographic record
Abstract
Recently, the occurrence and fate of pharmaceutically active compounds (PhACs) in the aquatic environment was recognized as one of the emerging issues in environmental chemistry and as a matter of public concern. Residues of PhACs have been found as contaminants in sewage, surface, and ground- and drinking water samples. Since June 2000, a new long-term monitoring program of sewage, surface, ground- and drinking water has been carried out in Berlin, Germany. Samples, collected periodically from selected sites in the Berlin area, are investigated for residues of PhACs and related contaminants. The purpose of this monitoring is to investigate these compounds over a long time period to get more reliable data on their occurrence and fate in the different aquatic compartments. Moreover, the surface water investigations allow the calculation of season-dependent contaminant loads in the Berlin waters. In the course of the monitoring program, PhACs and some other polar compounds were detected at concentrations up to the microg/L-level in all compartments of the Berlin water cycle. The monitoring is accompanied and supported by several other investigations such as laboratory column experiments and studies on bank filtration and drinking water treatment using conventional or membrane filtration techniques.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".