Comparison of Adjusted Fluoride Concentrations Between Water Treatment Facilities and Endpoints in Alberta, Canada.
Bibliographic record
Abstract
OBJECTIVES: This investigation aimed to determine whether fluoride concentration in water at the user endpoint remains the same as at the adjusted source, i.e., water treatment facilities. METHODS: Daycares in Alberta, Canada, were used as the endpoint to measure fluoride concentration. They were randomly selected from a list of 400 licensed daycares provided by the Ministry of Children's Services. All water samples collected from the daycares were sent to the accredited Alberta Centre for Toxicology (ACFT) for analysis within 7 days of collection. ACFT used ion chromatography to determine fluoride concentration levels. Statistics analyses were conducted using the software SPSS 25. RESULTS: Water samples were collected from 141 daycares in 35 municipalities. In municipalities that adjust fluoride content, public water is supplied by 8 Alberta Environment & Parks regulated water systems. Fluoride concentration in water samples examined at the endpoint ranged from 0.58 mg/L to 0.79 mg/L. The differences between fluoride concentration at the water treatment facilities and the daycares ranged from -0.03 to 0.22 mg/L. CONCLUSIONS: This study confirms that the concentration of fluoride adjusted at water treatment facilities in Alberta is maintained at endpoints at the approximate optimal level of 0.7 mg/L.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".