Non-carcinogenic risk assessment to human health due to intake of fluoride in the groundwater in rural areas of Gonabad and Bajestan, Iran: A case study
Bibliographic record
Abstract
In this study, a field research regarding groundwater contamination with fluoride and its related health risks to human health was carried out in 39 rural areas of Gonabad and Bajestan, Iran, in 2017. The results indicated that fluoride levels in two rural areas exceeded the WHO guideline. A total of 55% and 4.7% of the studied rural areas in Gonabad and Bajestan, respectively, had fluoride levels below the minimum recommended value of WHO for fluoride (0.5 mg/L). In this article, chronic non-cancer risks to three different groups of people, adults, children, and infants, for exposure to the fluoride were assessed. Health risk index values for fluoride contamination for 44% and 90% of children and infants in rural areas of Gonabad and Bajestan, respectively, were more than unity (>1), which clearly reveals that these age groups at the studied areas are at the chronic health risk due to the intake of fluoride-containing water. The order of fluoride contribution to non-carcinogenic health risk among the studied age groups was infants > children > adults. Therefore, from a public health viewpoint, it would be prudent and important that risk reduction measures be implemented to diminish the total body burden of fluoride in residents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".