Phytoremediation of Zinc, Copper, and Lead Using Ipomoea Aquatica in Water Contaminants
Bibliographic record
Abstract
Lake Tempe in the Wajo Regency, South Sulawesi (Indonesia) is highly toxic due to metal pollution from industrial activities and the activities of residents living around the region. Zinc-contaminated water poses a potential threat to biotic communities. This research aims to develop phytoremediation technology to effectively remove toxic zinc from contaminated lake Tempe. The use of plants as phytoremediation agents to accumulate metals in polluted water is considered adequate because the method is environmentally friendly and presents economic value. This study was therefore designed to assess the phytoremediation potential of water spinach against zinc (Zn), copper (Cu), and lead (Pb). Water spinach was planted in Tempe lake contaminated with zinc (Zn), copper (Cu), and lead (Pb) metals, and the study was conducted for 30 days under natural conditions. Subsequently, the Tempe lake physicochemical properties, including pH, TDS, TSS, total nitrogen, total phosphate as P, and Zn content, were measured, before and after the phytoremediation process. The ability of plants to absorb zinc (Zn), copper (Cu), and lead (Pb) were assessed by the bioconcentration factor (BCF). The results showed that there was a correlation between the BCF value and the phytoremediation time. The longer the phytoremediation time, the higher the BCF value are obtained. Infra-Red (IR) data shows the presence of metal binding in plants with the functional groups C=S, C=N, and OH. Water spinach has the potential as a phytoremediation agent in remediating zinc (Zn), copper (Cu), and lead (Pb) metals in polluted lake Tempe water.
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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".