Removal of endocrine disrupting compounds from wastewater using polymer particles
Bibliographic record
Abstract
This study evaluated the use of particles of molecularly imprinted and non-imprinted polymers (MIP and NIP) as a wastewater treatment method for endocrine disrupting compounds (EDCs). MIP and NIP remove EDCs through adsorption and therefore do not result in the formation of partially degraded products. The results show that both MIP and NIP particles are effective for removal of EDCs, and NIP have the advantage of not being as compound-specific as the MIP and hence can remove a diverse range of compounds including 17-β-estradiol (E2), atrazine, bisphenol A, and diethylstilbestrol. Removal of E2 from wastewater was also tested to determine the effectiveness of NIP in the presence of interfering substances and natural organic matter. Removal of E2 from wastewater samples was high and increased with increasing NIP. NIP represent an effective way of removing a wide variety of EDCs from 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 | 0.000 |
| 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 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".