A Review on the Role of Chemical Nature of Fluoride on Human Health and Environment
Bibliographic record
Abstract
The review of this article is focusing on role of chemical nature of fluoride on human health and environment.Fluoride has both positive and negative effects on human health and environment.However this positive effect is felt when the fluoride concentration is low.At higher concentration the negative affects of fluoride far out way its positive effects.Millions of people all over the world presently suffer from a debilitating bone disease called skeletal fluorosis and also from dental fluorosis.Apart from dental and skeletal fluorosis fluoride also affects many vital organs of the body.Scientists and Doctors across the world have worked extensively on the health impacts of fluoride on humans and animals and found that fluoride has tremendous impact on the living system.The only remedy is prevention by keeping fluoride intake within the safe limits.Fluoride poisoning can be prevented or minimized by using alternative water sources, removing excess fluoride, and improving the nutritional status of the population at risk.Clinical data indicate that adequate calcium intake is clearly associated with a reduced risk of dental fluorosis.Vitamin C may also act as a safeguard against the risk.Therefore, measures to improve the nutritional status of an affected population, particularly in children, appear to be an effective supplement for an antidote against fluorosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".