Investigation on the behaviour of selected pharmaceuticals in the groundwater after infiltration of treated wastewater
Bibliographic record
Abstract
In a rural arid area without suitable receiving water the treated wastewater of a low loaded municipal wastewater treatment plant with full nutrient removal and additional post treatment steps is infiltrated into the unsaturated soil for groundwater recharge. Groundwater probes at increasing distances from the infiltration point have been sampled every two months over a period of 14 months. Beside conventional parameters (nutrients, carbon, and bacterial counts) samples were analysed for pharmaceutically active substances. Depending on and corresponding to their chemical structure and their fate during wastewater treatment, the selected substances showed different behaviour in the saturated zone of the groundwater. The antiepileptic Carbamazepine behaves very conservative and only is removed negligible even after long flow times within the subsurface zone. For other substances like the tranquilizer Diazepam or the analgesics Diclofenac a partial elimination during the different steps of wastewater treatment can be observed. Further degradation could be observed during the subsequent subsurface passage. In correlation with flow time additional removal of these substances from the aqueous phase can be observed. The musk substances Galaxolide and Tonalide were removed to some extend but not as good as the previous mentioned compounds.
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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 | 0.000 | 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".