Fluoride Content in Drinking Water and the Health Implications of Fluoride-Rich Water Consumption: An Overview of the Situation in Canada and Nigeria
Bibliographic record
Abstract
Fluoride is an inorganic monatomic anion of fluorine and forms part of essential reagents used by the chemical industry. It occurs naturally in several minerals and in trace quantities in water. Fluoride has been used to prevent tooth decay and in the treatment of osteoporosis. Extensive research has consistently demonstrated the safety and effectiveness of fluorides in the prevention of dental caries following the practice of water fluoridation. Despite these benefits, fluorides pose danger as an endocrine disruptor thus, affecting bones, brain, thyroid gland, pineal gland and blood sugar levels. In Canada, water fluoridation remains a contentious issue although dental decay constitutes the most common chronic disease. However, several Canadians are receiving the benefits of water fluoridation and about 1% have access to naturally fluoridated water. In Nigeria, the prevalence of dental caries has been documented to be greatly reduced following fluoridation of public water supplies in areas where the condition was endemic. Fluoride is therefore the only medicine added to public water, and at the recommended level, fluoride is safe and effective in the reduction of dental decay and poses no risk for health problems. Hence, this article highlighted fluoride content in drinking water and the health implications of consuming fluoride-rich water with a focus on the situation in Canada and Nigeria.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".