Determination of concentration of heavy metals and metalloids in grapes grown in Gonabad vineyards and assessment of associated health risks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Metals and metalloids are considered as major public health hazards, they are known to accumulate in fruits, which are heavily consumed by humans because of their unique sweet taste and potential health benefits. Therefore, the aim of this study was to measure the concentration of ten heavy metals and metalloids including arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), Iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in grapes samples grown in Gonabad vineyards and to estimate the associated health risks of metals in terms of chronic daily intake (CDI), and carcinogenic and non-carcinogenic risks by hazard quotient (HQ), hazard index (HI) and cancer risk (CR). The overall concentration of in red grapes were in range 0.07–0.5 (mean 0.14), 0.08–0.13 (mean 0.10), 0.07–0.13 (mean 0.09), 0.06–1.49 (mean 0.29), 0.52–4.12 (mean 1.65), 6.43–42.17 (mean 19.01), 0.89–4.04 (mean 1.89), 0.07–9.23 (mean 0.71), 0.07–0.37 (mean 0.18), 0.40–4.13 (mean 1.05) mg/kg dry weight for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively. Based on the results, cadmium for all samples and Pb in 64.7 % and As in 35.3% of the samples exceeded the FAO/WHO permissible limits. The estimated non-carcinogenic and carcinogenic risk indices showed that the results were lower than the critical value (1) and in acceptable range, respectively, therefore red grape is safe for consumption with no impact on the human health. The obtained data can be used in remediation techniques, as well as in implementing control measures of metals contamination in grapes.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it