Methylmercury Measurements in Dried Blood Spots from Electronic Waste Workers Sampled from Agbogbloshie, Ghana
Bibliographic record
Abstract
Abstract Biomonitoring methylmercury (MeHg) exposure is problematic in resource-limited settings and with difficult-to-access populations where traditional biomarker approaches present logistical, economic, and ethical issues. The present study aimed to validate the use of dried blood spots (DBS) to assess MeHg exposure in a real-world contaminated field setting. Whole-blood and DBS samples were collected from electronic waste workers (n = 20) from Agbogbloshie (Ghana) in 2017, and DBS were also artificially created in the laboratory using the field-collected blood. Whole-blood MeHg concentrations averaged 0.84 µg/L, which was not different from levels measured in the corresponding DBS samples (field-collected or artificially created). Whole-blood MeHg comprised 61% of the blood total Hg concentrations. Linear regression analysis revealed no differences in MeHg concentrations between whole-blood samples and field-collected DBS (slope 0.89, R2 = 0.94) and between field and laboratory DBS (slope 0.89, R2 = 0.96). The MeHg content in DBS punch blanks averaged 0.86 pg and thus was not of concern. These findings indicate that DBS are a suitable tool for assessing MeHg exposure in real-world environmental settings that may be heavily contaminated. Environ Toxicol Chem 2021;40:2183–2188. © 2021 SETAC
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".