The effect of soil types on the phytoremediation of heavy metals by <i>Phragmites australis</i>
Bibliographic record
Abstract
The remediation of heavy metals from contaminated sites by the application of phytoextraction is proving to be efficient and cost-effective. A pot experiment was conducted to ascertain the effect of soil types (sand, loam and clay) in the remediation of copper (Cu), chromium (Cr) and mercury (Hg) using Phragmites australis. The results obtained throughout 60 days of plant growth showed that the copper absorption in the roots of the plant in sandy, loamy and clayey soils was 47, 79.1 and 96 mg/kg, respectively. Chromium absorption in the roots of the plant in sandy, loamy and clayey soils was 136, 180 and 353 mg/kg, respectively, while mercury absorption in sand, loam and clay was 11.7, 14.8 and 19.3 mg/kg. The translocation factor for all metals in the three soil types was less than 1; however, the bioaccumulation factor in all cases was more than 1. The study concluded that the accumulation of chromium in the tissues of the plant was more pronounced than those of copper and mercury. Clayey soil proved to be the favourable soil required for the effective remediation of the metals into the tissues of a plant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".