ARSENIC BIOMONITORING IN RURAL NOVA SCOTIA, CANADA
Bibliographic record
Abstract
Background and Aims Groundwater arsenic remains a significant global public health concern. Rapid, portable, noninvasive biomonitoring methods to assess chronic exposures in human populations are not currently available. The purpose of this investigation was to survey arsenic exposures for selected subjects in communities with historically elevated groundwater arsenic concentrations. Collaborators tested biological samples for arsenic concentration and genetic damage to determine relationships among the laboratory test results and survey exposures. Methods Study participants (n=179) were from 2 Nova Scotia communities, selected in consultation with government hydrogeologists, with documented high levels of arsenic in drinking water from residential wells. Participants completed a previously validated, 24-hour recall dietary survey (adapted for Nova Scotia based on pilot study responses), and allowed collection of a well water sample, which was tested by inductively coupled plasma mass spectrometry. Other possible arsenic exposures (eg. medications, smoking) were also included in the survey. Results Water arsenic concentrations ranged from below detection limits (0.07 μg/L) to 309 μg/L (median: 4.00 μg/L, 95% CI: 1.03 – 8.27 μg/L), with 41% ≥10 μg/L. Daily water arsenic consumption ranged from 0 μg – 1,799 μg (median: 2.86 μg, 95% CI: 1.1 – 12.9 μg). Estimated daily food arsenic consumption ranged from 0 μg – 1,606 μg (median: 19.6 μg, 95% CI: 14.70 – 59 μg).Estimated total arsenic ingestion ranged from 0 μg –2,874 μg (median: 80.2μg, 95% CI: 53.6 – 125 μg). Linear regression analyses indicated that water arsenic concentration, rice consumption, and seafood consumption were significant predictors of total arsenic consumption (p-value <0.01), with no other exposure, demographic or location factors remaining significant in the final model. Conclusions The study population had 41% of water arsenic concentrations ≥10 μg/L (recommended maximum, World Health Organization), but well within reported levels for Canadian populations. The main sources of arsenic in this population were water, seafood and rice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".