Manure and iron oxide show potential for reducing uptake of arsenic and mercury in lettuce grown in a contaminated mining site
Bibliographic record
Abstract
The poor soil quality and high concentrations of potentially toxic elements (PTEs) found in gold mine tailings make them inappropriate for crop production. Assessing the viability of mine tailings for crop production after mining is essential because of the harmful impacts of these PTEs on food safety and human health. A 44-day pot experiment was conducted to test the effectiveness of different soil amendments in reducing the levels of PTEs at a decommissioned mining site in south-western Ghana. Compost, iron oxides, and poultry manure were applied individually or in combination to the mine soil in the pots. Lettuce ( Lactuca sativa L.) was subsequently planted in the pots. Upon reaching maturity, the lettuce was harvested, and an analysis of the nutrients and PTE contents in both the soil and plants was done. The uptake of PTE by lettuce was evaluated, and the transfer coefficients of the PTEs were determined. The addition of manure and iron oxide as distinct ameliorants significantly decreased the uptake of PTEs by lettuce. The application of manure led to a 93 % decrease in arsenic (As) bioaccumulation in lettuce. Iron oxide resulted in a notable 67 % decrease in the bioaccumulation of As in lettuce. The exclusive application of manure led to an 83 % reduction in Hg uptake by lettuce plants, while Co uptake experienced a 46 % increase. Utilizing manure and iron oxides could prove beneficial in enhancing soil quality and potentially reducing the uptake of arsenic and mercury by lettuce grown in the contaminated mining site.
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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".