Evaluating human health risks from exposure to agricultural soil contaminants using one- and two-dimensional Monte Carlo simulations
Bibliographic record
Abstract
The health and well-being of Indigenous Peoples are closely connected to the state of their lands. While natural soils are important for food security initiatives within these communities, they may also expose people to harmful contaminants. Consequently, this study – guided by Indigenous community members and leaders – evaluates the human health risks associated with contaminants in soils intended for agricultural purposes on Indigenous Peoples’ lands in regions of Australia and Canada. Soil samples were collected from 47 sites in seven locations and analyzed for metals, metalloids, and organochlorine pesticides. Non-carcinogenic and carcinogenic risks were assessed for children, youths, and adults using one- and two-dimensional Monte Carlo simulations. The results indicate that there is a non-carcinogenic risk of exposure to lead (Pb) for children (HQ = 1.83) in Australia and an oral ingestion risk due to inorganic arsenic (As) for children (HQ = 1.05) in Newfoundland. Carcinogenic risks from As exposure were also identified for children (R = 1.68 × 10 −5 ) and adults (R = 1.18 × 10 −5 ) in Newfoundland from oral ingestion. However, no non-carcinogenic or carcinogenic risk from dermal exposure was found for all tested contaminants. The results indicate a potential need for targeted interventions, such as soil remediation, when and where possible, or community education, to reduce exposure risks. • Monte Carlo simulations reveal risks from soils used for agricultural activities. • Children in Australia face non-carcinogenic risk from Pb in agricultural soils. • As in Newfoundland soils linked to carcinogenic risk for children and adults. • Soil remediation and ongoing education efforts are recommended to reduce exposure risks.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".