Evaluation of methodology for mercury exposure assessment with field and laboratory studies
Bibliographic record
Abstract
The threat of environmental mercury (Hg), particularly methylmercury (MeHg), exposure to the health of humans has been well documented. Thus, it is important to monitor exposure and body burden for public health protection. The first objective of this thesis was to characterize the risk of Hg exposure in two Canadian coastal communities: Grand Marian (n = 91) and St. Andrews/St. Stephen (n = 52), New Brunswick, Canada, using dietary questionnaires and hair analysis. Average Hg intakes and body burden were below the most conservative guidelines. We attributed these results to the low Hg concentrations found in the species commonly consumed: haddock, canned tuna, lobster and pollock (all below 0.2 mg/kg). The analytical method employed to determine Hg in hair, cold vapor atomic absorption (CV-AAS), required a bundle of 100-150 hair strands and involved lengthy chemical digestion procedures which reduce throughput. Direct solid introduction techniques minimize these weaknesses. Our second and third objectives were to evaluate two such methods: (1) combustion, gold amalgamation, atomic absorption spectrometry (C-GA-AAS) and (2) laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) for measuring total Hg in single hair strands. Hair samples with a wide range of Hg exposure were obtained from communities. A 1:1 relationship was observed between C-GA-AAS and the established CV-AAS for analysis of 1-cm hair segments. Additionally, the average relative standard deviation (RSD) of Hg between hair strands within an individual was 6.5 +/- 2.8%, thus justifying the use of a single hair strand for biomonitoring. With a limit of quantification of 0.10 ng of total Hg, a single hair strand with average weight of 0.5 mg and Hg concentrations of 0.2 mg/kg can be measured routinely. Using LA-ICP-MS, we showed that a single laser shot can sample hair material within 50 mum along a single hair strand which is equivalent to less than one day of
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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 | 0.058 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".