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Record W3000224833 · doi:10.1201/9781351046633-140

Arsenic exposure in the Canadian general population: levels of arsenic species measured in urine, and associated demographic, lifestyle or dietary factors

2019· book-chapter· en· W3000224833 on OpenAlexafffundabout
Annie St-Amand, S. Karthikeyan, Mireille Guay, R. Charron, A. Vezina, Kate Werry

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsHealth Canada
FundersGovernment of Canada
KeywordsArsenicUrineEnvironmental healthPopulationEnvironmental chemistryChemistryBiologyMedicineEndocrinology

Abstract

fetched live from OpenAlex

In Canada, the primary source of exposure to arsenic is food, followed by drinking water, soil, and air. Measuring inorganic arsenic in urine provides a measure of internal dose integrating all sources and routes of exposure. Urine samples were collected through the Canadian Health Measures Survey (CHMS) in 2009 to 2011 from 2538 respondents and analyzed for various inorganic related arsenic species (arsenite, arsenate, monomethylarsonic acid, dimethylarsinic acid, arsenocholine and arsenobetaine). The geometric mean of urinary dimethylarsinic acid (DMA) in the Canadian population was 3.5 (95% CI: 3.0–4.0) μg As L−1. Concentrations were significantly higher in children than in adults. Canadians who eat rice once or more per day have higher urinary concentrations. No association was found with sources of drinking water.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.863
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.320
Teacher spread0.204 · 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
Published2019
Admission routes3
Has abstractyes

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