Global Assessment of Emerging Contaminant Removal in Wastewater Treatment Plants: In Silico Hazard Screening and Risk Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pharmaceuticals and personal care products (PPCPs) are emerging contaminants (ECs), whose presence in the environment is of increasing concern due to their widespread use and possible detrimental effects on wildlife and humans. These chemicals may present multiple hazardous properties such as environmental persistence, toxicity, high mobility, and the potential for bioaccumulation. In this study, extended bibliographic research was conducted to characterize the removal efficiency (RE) of PPCPs in wastewater treatment plants (WWTPs) considering different technologies. Measured values of RE were collected from the literature or calculated for 251 compounds. The molecular structure of the 245 PPCPs were used as the input to generate predictions of multiple properties using several QSAR tools, such as the OECD Toolbox, OPERA, EPI Suite™, and QSAR-ME Profiler. These predictions were compared to regulatory thresholds to identify hazardous chemicals and to screen persistent, mobile and toxic (PMT) or persistent, bioaccumulative and toxic (PBT) substances. Finally, chemicals were prioritized by combining values of RE and QSAR predictions for multiple properties. A total of 16 out of the 245 molecules were prioritized as the most hazardous compounds to the aquatic environment and, among these, six were associated with potential risk due to their exposure concentrations reported in the literature.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it