The Michigan-West Africa Geohealth Hub: Environmental Exposures Due to Informal E-Waste Recycling Activities and the Health of Workers
Bibliographic record
Abstract
Overview: The West Africa-Michigan CHARTER II GEOHealth Hub, jointly funded by the US NIH/FIC and Canada’s IDRC, integrates research and research training activities of West African Anglophone and Francophone institutions, with support from the University of Michigan and McGill University. Research Goal: To increase multi-disciplinary understanding of the risks associated with waste recycling, and to use study findings to inform evidence-based implementation activities and policy options at multi-levels. Specific Objectives: Include: 1) characterize work-related, time-varying, job-specific exposures of electronic waste recycling workers at the Agbogbloshie site, and assess biological markers of dose, to metals, organic compounds, and markers of combustion products; 2) provide estimates of potentially increased lifetime, work-exposure-associated cancer risks; and, 3) evaluate associations of exposures with measures of acute and chronic respiratory morbidity in workers. Methodology: A longitudinal design in which we enrolled a combined total of 151 study participants over a 3-week period. We collected and are analyzing, repeated measures across seasons for each participant: 1) biological samples for a) metals, b) organic compounds including flame retardants, polycyclic aromatic hydrocarbons, dioxin-related compounds, and 2) personal air monitoring, through a combination of real-time measurements and analysis of size-specific samples collected on filters, including markers of combustion products. Results: Preliminary filter-based data show that e-waste workers have breathing zone PM2.5 concentrations of 135 ± 188 µg m-3 (mean ± st. dev., n = 89) compared to 45 ± 18 µg m-3 (n = 43) of controls; these worker exposures are considerably higher than levels obtained using area monitoring at the waste site, e.g., 84 ± 24 µg m-3 (n = 9). Based on real-time measurements, burning tasks resulted in exceptionally high PM2.5 exposures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".