Evaluation of Fluoride Concentration in Drinking Water of Some Cities within Anbar Province, Iraq
Bibliographic record
Abstract
A fluoride atom (F) has a negatively-charged ion called a fluoride ion. The element fluoride can also be found in a compound called fluoride. Many countries with high rates of dental fluorosis naturally contain high levels of fluoride in their minerals and water supplies. The fluoride ion concentrations were determined by collecting drinking water samples twice a month from (July/2020 - to June/2021) from 4 main cities in Anbar Province in Iraq, during the rainy and dry seasons. The fluoride concentrations were then analyzed and compared with standard guides of the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). The objective of this study is to evaluate the fluoride concentrations in Ramadi, Fallujah, Khalidiyah, and Heet cities. The results showed small variations ranging from (0.041 mg/l) to (0.051 mg/l) when these results are compared with Environmental Protection Agency (EPA) and World Health Organization (WHO) standard limitations. the recommended standard limitations of (EPA) and (WHO) for fluoride concentration range from (0.4 - to 1.5) mg/l, therefore the drinking water of these cities is comfortable to drink, but it may cause some problems, such as dental caries, dental fluorosis, colorless teeth yellowish, and blackish teeth. The results are also less than the typical concentration limitations, which, is (0.7 mg/l). To avoid the danger of poisoning, adding fluoride to water is not recommended unless it has been recommended by a professional in the food system or a dentist. However, humans can also get fluoride from sources other than water, and the quantity of fluoride they consume is different from one person to another.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".