Biomonitoring of Mercury Exposure with Single Human Hair Strand
Bibliographic record
Abstract
Hair samples continue to be used extensively for biomonitoring of mercury (Hg) exposure. Routine methods require a bundle of 100-150 hair strands and involve chemical digestion. Recently, Hg analyzers that combine combustion, gold amalgamation, and atomic absorption spectrometry (C-GA-AAS) became commercially available. This method was shown to provide quick and sensitive measurements in solid samples such as hair. The objective of this study was to validate C-GA-AAS for measuring total Hg in single hair strands as an alternative method for Hg biomonitoring. Hair samples from 12 women with a wide range of Hg exposure were obtained from two projects conducted in Brazil and Canada. A 1:1 relationship was observed between C-GA-AAS and the established cold vapor atomic absorption spectrometry (CV-AAS) for analysis of 1-cm segments from a bundle of hair. For individual hair variability, the average relative standard deviation (RSD) of Hg between hair strands was 6.5 +/- 2.8%, thus justifying the use of single hair strand for biomonitoring. With a limit of quantification of 0.10 ng of total Hg, a single hair strand can be used to assess monthly exposure. This technique will facilitate routine biomonitoring and thus help prevent Hg poisoning among the public.
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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 | 0.000 | 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) | 0.001 | 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".