Bibliographic record
Abstract
Triclosan is an antimicrobial agent and one of the many emerging contaminants that is frequently detected in the environment. Triclosan is an endocrine disruptor that is able to accumulate in living organisms (i.e., fish, algae, humans). Triclosan can transform into various transformation products that are potentially toxic and can contribute to bacteria resistance against antibiotics. This research aimed at minimizing the accumulation of toxic by–products in open–water treatment wetlands for the safe transformation of triclosan. This work focused on triclosan phototransformation, and algae uptake and transformation. The results showed that the half–life of triclosan was generally shorter in solutions with a high pH (where the pH is greater than triclosan’s pKa at 8.2), and low dissolved organic carbon (DOC) concentration. However, in treatment wetlands, where the pH is around 7, triclosan phototransformation cannot be maximized unless the DOC is reduced, and the pH is increased. The use of Euglena gracilis strain Z was studied for its potential to (i) provide an additional transformation pathway for triclosan and (ii) naturally increase the pH of the water via photosynthesis. A series of multiple laboratory–scale algae reactors was carried out to investigate triclosan fate in open–water treatment wetlands. The pH of the algae–containing reactors increased during the 25–day experimental period for wetland water, autoclaved wetland water, and growth media. More than 90% triclosan concentration decrease was also observed in the reactors that used continuous (white) light. Data on kinetics, pathway apportionment, and transformation products showed that phototransformation, algae uptake and transformation, microbial transformation, and adsorption (at low pH) all contributed to triclosan elimination in these lab–scale algae reactors. Altogether, these results suggest that triclosan removal can be accomplished with open-water treatment wetlands while minimizing the accumulation of triclosan’s transformation products.
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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 | 0.001 | 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".