Modeling exposure risk and prevention of mercury in drinking water for artisanal-small scale gold mining communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of this study was to evaluate the age-differentiated health risks associated with exposure to mercury in drinking water from artisanal small-scale gold mining (ASGM) sites on nearby communities in Yolombo, Colombia. In 2017, nine samples were collected from a local regulatory agency to report mercury concentrations in locations near mining sites. We performed a risk assessment to find 100% of the water samples collected downstream of mining sites exceed Hazard Quotient (HQ) risk thresholds (set by the US-Environmental Protection Agency and Health Canada). HQ model, coupled with global sensitivity and uncertainty analysis (GSUA), was used to conclude infants as the most vulnerable, with 50% of the population exceeding HQ thresholds. Length of exposure was the most significant input that contributed to risk variance, explaining 30-55% of risk across all age groups. Monte-Carlo filtering was used to identify effective strategies to reduce the number of individuals exceeding allowable HQ thresholds. After Monte-Carlo filtering intervention strategies, all individuals are below HQ thresholds. This work shows the importance of combining risk assessment tools with sensor data to inform the need for filters, stakeholder education, and alternative mining approaches to gain a multi-perspective risk approach. This work provides a valuable risk and decision modeling methodology and baseline information to gain a deeper understanding of the probability of experiencing detrimental health effects from water contamination in ASGM communities.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it