Phytoremediation of Soil Contaminated with Lead (Pb) and Zinc (Zn) Using <i>Chromolaena odorata</i> (L.) under Greenhouse Condition
Why this work is in the frame
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Bibliographic record
Abstract
Elevated soil heavy metal level due to anthropogenic activities has continued to raise concerns in recent times. Phytoremediation is gaining popularity among environmentalists worldwide for its cost-effectiveness and environmentally friendly approach to removal of heavy metals from soil using plants. This study investigated potential of weed, Chromolaena odorata (L.), in remediating soil contaminated with lead (Pb) and zinc (Zn). The experiment involved growing C. odorata as potted plants in soil with varying concentrations (20-100 mg/kg) of Pb and Zn alongside a control treatment (0 mg/kg). Seedling survival was unaffected by both metals. In comparison to the control, there was no significant effect of Pb on number of leaves at 20-60 mg/kg but significantly increased it at 80-100 mg/kg. Zn grown plants had significantly reduced number of leaves at all the concentrations applied. Pb did not significantly affect stem girth while Zn led to its significant reduction at 60-100 mg/kg. Pb did not cause significant effect on leaf area except at 80 mg/kg where there was a significant increase, while it was not significantly affected by Zn. Both metals decreased root length without statistical difference from the control while number of roots was not affected. Fresh and dry weight values of plant parts were higher under contamination than the control. This was significant at 80-100 mg/kg for leaf and stem, and at 80 mg/kg for root. The plant was generally more tolerant to Pb than Zn in terms of growth. There were significantly higher concentrations of both metals in the parts of plants grown in metal contaminated soil than the control. Heavy metals were accumulated in plants grown with heavy metals than the control. C. odorata is a potential candidate for phytoremediation of soil contaminated with Pb and Zn, and can survive up to 100 mg/kg.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it