Removal of pharmaceutically active compounds in nitrifying–denitrifying plants
Bibliographic record
Abstract
The behaviour of nine pharmaceutically active compounds (PhACs) of different diagnostic groups is studied during a nitrifying-denitrifying process in an activated sludge system. The compounds selected cover a wide range of frequently used substances such as anti-epileptics (carbamazepine), tranquillisers (diazepam), anti-depressants (fluoxetine and citalopram), anti-inflammatories (ibuprofen, naproxen and diclofenac) and estrogens (estradiol and ethinylestradiol). The main objective of this research is to investigate the effect of acclimation of biomass on the removal rates of these compounds, either by maintaining a high sludge retention time or at long-term operation. The removal rates achieved for nitrogen and carbon in the experimental unit exceed 90% and were not affected by the addition of PhACs. Carbamazepine, diazepam and diclofenac were only removed to a small extent. On the other hand, higher removal rates have been observed for naproxen and ibuprofen (68% and 82%), 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".