The Influence of Chlorpyrifos on the Nonenzymatic Antioxidants Content in Macrophytes Leaves
Bibliographic record
Abstract
Water pollution can be moved or limited by macrophytes in a phytoremediation process. The presence of insecticides in the plant life environment may affect physiological processes and contribute to the formation of secondary oxidative stress in plant tissues. To protect against oxidative stress, macrophytes have developed a system of mechanisms consisting of nonenzymatic and enzymatic antioxidants. The influence of various concentrations of chlorpyrifos on the nonenzymatic system in Canadian waterweed (Elodea canadensis Michx.), needle spikerush (Eleocharis acicularis) and water mint (Mentha aquatica L.) was studied. The differences in the value of the total concentration of polyphenols and flavonoids, as well as analysis of chlorophyll-a, chlorophyll-b, anthocyanin and carotenoid concentrations were determined in leaves. Research indicated a significant increase in the content of polyphenols and flavonoids in a solution with the highest concentration of chlorpyrifos while the opposite tendency was observed after analyses of the main assimilating pigments of plant tissues. It was concluded that aqueous plants exposed to toxic insecticide molecules created a defensive mechanism by nonenzymatic antioxidant systems and the amount of low-molecular weight compounds depended on the pollutant concentration which influenced biosynthesis mechanisms in plant cells.
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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".