Evaluation of Two In Vitro Protocols for Determination of Mercury Bioaccessibility: Influence of Mercury Fractionation and Soil Properties
Bibliographic record
Abstract
Soil ingestion by children can be a significant exposure pathway to mercury (Hg). Unfortunately, no reliable in vivo results for Hg oral bioavailability determination in soils have been published. In vitro extractions enable the assessment of metals' bioaccessibility, which is an estimate of oral bioavailability. Therefore, the goal of this study was to evaluate two in vitro protocols (CDM (Camp Dresser and Mc Kee Inc.) and IVG (In Vitro Gastrointestinal)) for determination of Hg bioaccessibility in soils using pure Hg-compounds, reference materials (CRM 025-050 and ERM-CC580), and field-collected soils with elevated Hg concentrations. The influence of Hg fractionation and soil properties on Hg bioaccessibility was also investigated. In the field-collected soils, IVG bioaccessibility ranged from 1.5 to 7.5%, and was always below 3.15%, using the CDM method. Mercury bioaccessibility in CRM 025-050 was 61.5 and 34.7%, using IVG and CDM protocols, respectively, whereas Hg bioaccessibility was much lower in the certified sediment sample ERM-CC580 (<7%). Overall, the CDM protocol resulted in lower Hg bioaccessibility values. The water-soluble and exchangeable Hg fraction was highly correlated with gastrointestinal bioaccessibility (r=0.99, p<0.001 for both methods) and this fraction could be a potentially good indicator of Hg bioaccessibility. Because the IVG method is less time-consuming than the CDM protocol and includes organic physiological components which seem to increase Hg bioaccessibility, it might be preferred for determination of Hg bioaccessibility.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".