Removal of pharmaceutically active compounds from water systems using freeze concentration / by Yuanyuan Shao.
Bibliographic record
Abstract
"In the last few years, there has been a growing concern in the occurrence of pharmaceutically active compounds in the aquatic environment. Just in Europe, more than 3000 prescription and non-prescription drugs are used by human and animals and more than 80 drugs have been detected in municipal wastewater treatment plant effluent, surface water, groundwater, and in a few isolated cases, in drinking water, some at alarmingly high concentrations. Although no known human health effects have been associated with exposure to drinking water containing trace concentrations of drug residues, there is concrete evidence that these drug residues could cause numerous adverse health effects on aquatic life, even at very low concentrations. Municipal wastewater treatment plant effluents have been identified as the major source of drug residues in surface waters. Conventional wastewater treatment systems cannot effectively remove pharmaceutically active compounds. The suitability of distinct wastewater treatment processes for the elimination of drug residues has not been studied. Freezing has been used successfully to treat various wastewaters.
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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) | 0.002 | 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".