Insights into Long-Term Toxicity of Triclosan to Freshwater Green Algae in Lake Erie
Bibliographic record
Abstract
This study explored the long-term impacts of a pulse disturbance of triclosan on five nontarget green algae in Lake Erie. Comprehensive analyses were performed using multiple physiological end points at community and subcellular scales. The toxic mechanism of triclosan in a wide range of concentrations was analyzed. The diverse sensitivity of algae species and complex interrelationships among multiple end points were revealed. The results showed the taxonomic groups of algae were the key issue for sensitivity difference. High doses of triclosan caused irreversible damage on algae, and environmentally relevant doses initiated either inhibition or stimulation. Smaller cells had higher sensitivity to triclosan, while larger cells had a wider size variation after exposure. Colonial cells were less sensitive than unicells. For chlorophyll, there were better dose–response relationships in Chlorococcum sp., Chlamydomonas reinhardtii CPCC 12 and 243 than Asterococcus superbus and Eremosphaera viridis . For chlorophyll fluorescence, F v / F m was the most sensitive parameter, and q N was more sensitive than q P . Triclosan showed long-term effects on biochemical components, such as lipids, proteins, and nucleic acids. The findings will be helpful for a systematic and complete assessment of triclosan toxicity in natural waters and the development of appropriate strategies for its risk management.
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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 | 0.001 |
| 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".