Municipal Landfill Leachate Treatment for Metal Removal Using Water Hyacinth in a Floating Aquatic System
Bibliographic record
Abstract
Experiments were carried out to investigate the ability of water hyacinth (Eichhornia crassipes) to remove five heavy metals (cadmium, chromium, copper, nickel, and lead) commonly found in leachate. All experiments were conducted in batch reactors in a greenhouse. It was found that living biomass of water hyacinth was a good accumulator for copper, chromium, and cadmium. The plants accumulated copper, chromium, and cadmium up to 0.96, 0.83, and 0.50%, respectively, of their dry root mass. However, lead and nickel were poorly accumulated in water hyacinth. Also, nonliving biomass of water hyacinth dry roots showed ability to accumulate all metals, except Cr(VI), which was added in anionic form. The highest total metal sorption by nonliving dry water hyacinth roots was found to take place at pH 6.4. The current research demonstrates the potential of using water hyacinth for the treatment of landfill leachate containing heavy metals.
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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".