Specific types of wastewater pollution in Ostrava and possibilities of decontamination through wastewater treatment plants
Bibliographic record
Abstract
This paper provides an introduction to the problem of the occurrence of some groups of micropollutants in wastewater, namely pharmaceutically active compounds (PhAC) and drugs in the context of wastewater treatment in Ostrava (290,000 inhabitants). Wastewater treatment is an essential service that ensures the reduction of pollutants in wastewater, while also protecting human health and the environment. In Europe, most wastewater enters the sewerage system and is discharged to a wastewater treatment plant, from where it is further discharged into rivers, lakes or coastal areas. Recently, people have been focusing more on pollutants in wastewater that are not targeted by WWTP, i.e., so-called micropollutants, which are, for example, pharmaceutically active compounds, drugs, or their metabolites. The risk of these groups of micropollutants in water is, for example, the possibility of exposure to aquatic organisms or bioaccumulation in food chains. The discharge of treated wastewater from the WWTP is the central route for PhAC to enter surface waters, as current technologies for decontamination are not yet designed. On the other hand, WWTPs act as primary barriers against the spread of micropollutants. One of the basic steps in designing a decontamination technology is to know the composition of the local wastewater.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".