Gastrointestinal Microbes Increase Arsenic Bioaccessibility of Ingested Mine Tailings Using the Simulator of the Human Intestinal Microbial Ecosystem
Bibliographic record
Abstract
It is widely accepted that the use of total metal concentrations in soil overestimates metal risk from human ingestion of contaminated soils. In vitro simulators have been used to estimate the fraction of arsenic present in soil that is bioaccessible in the human digestive track. These approaches assume that the bioaccessible fraction remains constant across soil total metal concentrations and that intestinal microbiota do not contribute to arsenic release. Here, we evaluate both of these assumptions in two size fractions (bulk and <38 microm) of arsenic-rich mine tailings from the Goldenville, Lower Seal Harbour, and Montague Gold Districts, Nova Scotia. These samples were evaluated using an in vitro gastrointestinal model, the Simulator of the Human Intestinal Ecosystem (SHIME). Arsenic bioaccessibility, which ranged between 2 and 20% in the small intestine and 4 and 70% in the colon, was inversely related to total arsenic concentration in the mine tailings. Additionally, arsenic bioaccessibility was greater in the bulk fraction than in the <38 microm fraction in the small intestine and colon while colon microbes increased the bioaccessibility of arsenic in mine tailings. These results suggest that the practice of using a constant percent arsenic bioaccessibility across all metal concentrations in risk assessment should be revisited.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".