The Use of Macrophytes for the Removal of Chlorpyrifos from the Aquatic Environment
Bibliographic record
Abstract
Phytoremediation is one of the effective technologies for removing pollutants from the aquatic environment. Toxic compounds such as chlorpyrifos can affect the physiological processes of aquatic plants, causing secondary oxidative stress in plant tissues. Macrophytes, like other organisms inhabiting the contaminated ecosystem, have developed a system of defense mechanisms, thanks to which plants can still exist in their natural ecosystem. Our research is a summary of the previously presented results of the effectiveness of purifying contaminated water with chlorpyrifos in the phytoremediation process and the second type of phytoremediation supported by microorganisms, which intensify the process of removing contaminants from the environment. This research concerned changes in nonenzymatic and enzymatic antioxidants in Canadian seaweed, needle spikerush and water mint caused by chlorpyrifos. The research determines changes in the total concentration of polyphenols, flavonoids and dyes (chlorophyll A, chlorophyll B, anthocyanins and carotenoids) as well as differences in the activity of guaiacol peroxidase and glutathione S-transferase. The analysis of the results showed an increase in the content of polyphenols and flavonoids. The reverse trend was observed in the case of the pigment content. The appearance of chlorpyrifos in the environment caused an increase in the activity of the examined enzymes. The process involving microorganisms that were obtained from places contaminated with pesticide proved to be more effective. This shows the cooperation of species living in an investigated ecosystem.
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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) | 0.001 | 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".