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Record W4200633334

Comparison of Adjusted Fluoride Concentrations Between Water Treatment Facilities and Endpoints in Alberta, Canada.

2021· article· en· W4200633334 on OpenAlexaboutno aff
Silvia Gianoni‐Capenakas, Jessica Popadynetz, John G. Younger, T. A. White, Pamela Hodgkinson, Steven Patterson, Camila Pachêco‐Pereira, Rafael Figueirêdo

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsnot available
Fundersnot available
KeywordsFluorideChristian ministryEnvironmental scienceEnvironmental chemistryChemistryPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.221
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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