Effect of brewery spent diatomite sludge on trace metal availability in soil and uptake by wheat crop, and trace metal risk on human health through the consumption of wheat grain
Bibliographic record
Abstract
Soil plays a crucial role in food safety as it determines the composition of food at the beginning of the food chain. However, the quality of soil resources in terms of their potential impact on human health caused by harmful elements is poorly understood in Ethiopia due to lack of reliable and appropriate experimental data. In this study, a field experiment was conducted to evaluate trace metal accumulation in soils amended with brewery spent diatomite sludge (BSDS) in comparison to control, recommended inorganic fertilizers (RIF) and integrated BSDS & RIF (BSDS:RIF). Trace metals uptake by wheat crop and the degree of soil contamination, and human health risk were investigated. BSDS application significantly increased the concentration of trace metals (Cu, Zn, Pb and Ni) in soil compared to that in the control, the RIF, and BSDS:RIF applications. It also significantly increased the concentration of Cu, Zn, Pb and Ni in the wheat grain compared to that in the control and the RIF, but the increase was not significantly different from that in BSDS:RIF amendment. All trace metal concentrations in soil and wheat grain (except Pb in wheat grain) were below the maximum permissible limits in some European countries of agricultural soils, and the maximum permissible limit of FAO/WHO. The degree of soil contamination from each of the treatments was below the maximum acceptable degree of contamination. The Health Risk Index (HRI) was <1 for all metals in all treatments. Thus, we conclude that consumption of wheat grain grown on soils amended with BSDS and BSDS:RIF has no human risks and has low likelihood of human exposure to trace metals.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".