Health Risk Assessment and Nickel Content in Soils, Rice (Oryza Sativa L.) and Wheat (Triticum Aestivum L.) Grown in Damietta Governorate, Egypt
Bibliographic record
Abstract
Nickel (Ni) concentration in soils is highly depended on the parent materials and the types of pollutant sources that plays a beneficial role in plant growth however; at high concentration it may cause toxicity for plants and creating hazards to animals and human. Therefore, this study aimed to estimate the levels of Ni in soils, straw and grain of rice and wheat plants grown in the soils contaminated with Ni and evaluate its effect on human health. In the surface soil layers the total (31.4 ±8.02 mg kg-1) and available Ni concentration (3.10 ±0.91 mg kg-1) are slightly higher by 1.25 ±0.14 and 1.24 ±0.25 fold respectively, than the subsurface layers. Available Ni increased linearly with increasing Ni in soil (r = 0.91). A significant positive correlation was found between available Ni and soil OM content (r = 0.89), while a significant negative correlation was observed for soil CaCO3 percent (r = - 0.72) and soil pH (r = - 0.90). Rice Ni content of the straw (2.1 ±0.32 mg kg-1) and grains (0.44 ±0.07 mg kg-1) were significantly correlated with soil total Ni (r = 0.89 and 0.86) and available Ni (r = 0.84 and 0.74), respectively. Wheat Ni content of straw (1.68 ±0.28 mg kg-1) and grains (0.28 ±0.04 mg kg-1) were significantly correlated with soil total Ni (r = 0.87 and 0.81) and available Ni (r = 0.84 and 0.85), respectively. By increasing straw Ni content grains increased (r = 0.89 for rice and r = 0.95 for wheat). Grains of rice and wheat exhibited lower Ni concentration than that of the straw (20.9% ± 1.64 and 16.7% ± 1.04, respectively). According to FAO/WHO rice and wheat grains contain normal Ni concentration and no evidence of possible potential human health risk with grains consumption.
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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 | 0.001 | 0.001 |
| 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".