Proof of concept for a passive sampler for monitoring of gaseous elemental mercury in artisanal gold mining
Bibliographic record
Abstract
Mercury emissions from artisanal gold mining operations occurring in roughly 80 developing countries are a major workplace health hazard for millions of people as well as the largest contributor to global mercury pollution. There are no portable, cheap, and rapid methods able to inform workers or health practitioners of mercury exposure on site in remote locations. In this work, a proof of concept for a miniaturized mercury sampler, prepared by the direct reduction of gold into the porous nanostructures of Vycor glass (PVG), is introduced. Mercury retention on the PVG/Au sampler induces significant color changes, due to the formation of Au-Hg amalgam that affects the surface plasmon resonance characteristics of the material. The color change can potentially be quantified by the analysis of pictures obtained with a cell phone camera rapidly and onsite. Laboratory experiments showed the viability of using PVG/Au as passive sampler for monitoring of Hg°. PVG/Au samplers were then deployed in an artisanal and small-scale gold mining (ASGM) operations in Burkina Faso and it was able to indicate personal mercury exposures. The amount of mercury quantified in the samplers for all miners was higher than the current personal exposure limit set by the US Occupational Safety & Health Administration (OSHA).
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 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".