Evaluation of Physicochemical Characteristics in Drinking Water Sources Emphasized on Fluoride: A Case Study of Yancheng, China
Bibliographic record
Abstract
In this study, the concentration of fluoride and the associated health risks for infants, children, and adults were analyzed and compared for three drinking water sources in Yancheng City, Jiangsu Province, China. To analyze the relationship between the water quality parameters of pH, fluoride (F−), sulfate (SO42−), chloride (Cl−), total dissolved solids (TDS), total alkalinity (TAlk), sodium (Na+), and potassium (K+), statistical analyses including correlation analysis, R-mode cluster analysis and factor analysis were performed based on monthly data from the year 2010 to 2015. The results indicated: (1) Fluoride concentrations in the drinking water sources ranged from 0.38 to 1.00 mg L−1 (mean = 0.57 mg L−1) following the order of Tongyu River > Yanlong Lake > Mangshe River; (2) fluoride concentrations in 22.93% of the collected samples were lower than 0.5 mg L−1, which has the risk of tooth cavities, especially for the Mangshe River; (3) the fluoride exposure levels of infants were higher than children and adults, and 3.2% of the fluoride exposure levels of infants were higher than the recommended toxicity reference value of 122 μg kg−1 d−1 as referenced by Health Canada, which might cause dental fluorosis issues; (4) the physico-chemical characteristics are classified the into four groups reflecting F−- TAlk, Na+-K+, SO42−-Cl−, and pH-TDS, respectively, indicating that fluoride solubility in drinking water is TAlk dependent, which is also verified by R-mode cluster analysis and factor analysis. The results obtained supply useful information for the health department in Yancheng City, encouraging them to pay more attention to fluoride concentration and TAlk in drinking water sources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".