Multiple Metals Exposures amongst Community Residents in the Cadmium-Contaminated Mae Sot District, Thailand
Bibliographic record
Abstract
Cadmium exposures in the Mae Sot District of Thailand are amongst the highest worldwide. Cadmium occurs as a byproduct of zinc mining in the region and is spread across the region via anthropogenic activities such as agriculture, and thus contamination of local waterways, soils and produce is ubiquitous. In addition to cadmium, zinc mining can be associated with a number of other potentially toxic elements. There exists some data to suggest that other elements contaminate the Mae Sot ecosystem, yet human exposures have yet to be established. The objective of the current study was to increase understanding of human exposures to multiple metals in the Mae Sot District. Based on a study involving 7,697 participants surveyed in 2004, here we focused on subset of 50 participants who were selected as part of a study focused on exposures, health outcomes (focus: renal), and epigenetics. Blood and urine were collected, and analyzed for 15 elements via ICPMS. As expected urinary cadmium (median: 5.0 ug/L; interquartile range: 2.7-13.8) and blood cadmium (3.5, 2.0-5.8 ug/L) were higher than reference range values and also significantly correlated (r=0.76). A number of other elements were also found at concentrations deemed to exceed background levels, and these included urinary arsenic (63.6, 35.6-105.2 ug/L), copper (33.1, 17.9-56.8 ug/L), and zinc (923.8, 631.8-1,947). Overall these results document that residents in the Mae Sot District of Thailand are not only exposed to cadmium, but to elevated levels of several other potentially toxic elements. Future work is needed to understand how exposures to other elements, along
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".