Addressing arsenic bioaccessibility in ecological risk assessment: A novel approach to avoid overestimating risk
Bibliographic record
Abstract
The risk of arsenic exposure to deer mice (Peromyscus maniculatus) living in areas of naturally and anthropogenically elevated arsenic levels was determined using three separate calculations of arsenic daily intake: Estimated daily intake (EDI), bioaccessible EDI (BEDI), and actual daily intake (ADI). The present work is of particular interest, because the risk assessments were determined for animals naturally exposed to arsenic. Gastric fluid extraction was used to obtain bioaccessibility data for soil and plant samples collected from three study sites (background, mine forest, and tailings) in Yellowknife (NT, Canada). Calculations using the EDI indicated that deer mice living in tailings habitat (average soil arsenic concentration, 1,740 +/- 2,240 microg/g) should have been experiencing serious health effects as a result of their exposure to arsenic. Using BEDI and ADI in the risk assessment calculation, however, resulted in an order-of-magnitude decrease in calculated risk. In addition, results calculated using the BEDI and ADI were not significantly different, suggesting that using bioaccessibility provides a more realistic estimate of potential risk. The present results provide evidence that the use of EDI in traditional risk assessments may seriously overestimate the actual risk, which in some instances may result in expensive and unnecessary clean-up measures.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".