Analyzing the Biochemical Alteration of Green Algae During Chronic Exposure to Triclosan Based on Synchrotron-Based Fourier Transform Infrared Spectromicroscopy
Bibliographic record
Abstract
The study explored the chronic toxicity of triclosan to green microalga Chlorococcum sp. under multiple interactions among multiple environmental conditions. This is the first study on chronic algal toxicity to combine synchrotron-based Fourier transform infrared spectromicroscopy, factorial analysis, principal component analysis, and stepwise-cluster analysis. Such a combination helps to reveal the toxic mechanism at the molecular level and explore the inner correlationship among multiple environmental conditions. In the 120-h test, nitrogen content became the most significant factor of the physiochemical properties. Some insignificant factors in the 48-h test became significant in the 120-h test. Temperature * nitrogen content, temperature * phosphorus content, and pH * phosphorus content were the most significant two-order interactions. Temperature * pH * NaCl concentration and temperature * NaCl concentration * phosphorus content were the most significant three-order interactions. More high-order interactions became significant in the 120-h test, indicating the complexity and impacts of all the factors may increase when time was extended. The chronic toxicity of triclosan presented more distinguishable variations among treatments based on biochemical alterations. These results demonstrate that the sensitivity and fragility of algae to triclosan can be amplified with time extension. Long-term exposure can be applied to better evaluate and predict the environmental toxicity behavior of triclosan. It can also help with environmental evaluation and risk management of real-world triclosan toxicity.
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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".