Mercury exposure in a low-income community in South Africa
Bibliographic record
Abstract
OBJECTIVES: To establish whether a specific community in a gold mining area, with potentially associated small-scale gold mining activities, was exposed to mercury. METHODS: The community was situated in Mpumalanga, where some potential sources of mercury emissions may have an impact. Adults >or=18 years were considered eligible. Biological monitoring, supported by questionnaires, was applied. Thirty respondents completed the questionnaire which covered demographics, energy use, food and water consumption, neurological symptoms, and confounders such as alcohol consumption and brain injuries. Mercury levels were determined in 28 urine and 20 blood samples of these respondents. RESULTS: Three (15%) of the blood samples exceeded the guideline (<10 microg/l) for individuals who are not occupationally exposed, while 14 (50%) of the urine samples exceeded the guideline for mercury in urine (<5.0 microg/g creatinine) for those not exposed occupationally. The cause of these elevated levels is unknown, as only 20% of respondents indicated that they used coal as an energy carrier. Furthermore, nobody from the community was reportedly formally employed in a goldmine. Nineteen (63%) respondents consumed locally caught fish, while 20 (67%) drank water from a river. CONCLUSIONS: Some individuals in this study may be occupationally exposed to mercury through small-scale gold mining activities. As primary health facilities will be the first point of entry for individuals experiencing symptoms of mercury poisoning, South African primary health care workers need to take cognisance of mercury exposure as a possible cause of neurological symptoms in patients.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".