Improving Risk Assessment Calculations for Traditional Foods Through Collaborative Research with First Nations Communities
Why this work is in the frame
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Bibliographic record
Abstract
As industrial development is increasing near northern Canadian communities, human health risk assessments (HHRA) are conducted to assess the predicted magnitude of impacts of chemical emissions on human health. One exposure pathway assessed for First Nations communities is the consumption of traditional plants, such as muskeg tea (Labrador tea) (Ledum/Rhododendron groenlandicum) and mint (Mentha arvensis). These plants are used to make tea and are not typically consumed in their raw form. Traditional practices were used to harvest muskeg tea leaves and mint leaves by two First Nations communities in northern Alberta, Canada. Under the direction of community elders, community youth collected and dried plants to make tea. Soil, plant, and tea decoction samples were analyzed for inorganic elements using inductively coupled plasma-mass spectrometry. Concentrations of inorganic elements in the tea decoctions were orders of magnitude lower than in the vegetation (e.g., manganese 0.107 mg/L in tea, 753 mg/kg in leaves). For barium, the practice of assessing ingestion of raw vegetation would have resulted in a hazard quotient (HQ) greater than the benchmark of 0.2. Using measured tea concentrations it was determined that exposure would result in risk estimates orders of magnitude below the HQ benchmark of 0.2 (HQ = 0.0049 and 0.017 for muskeg and mint tea, respectively). An HHRA calculating exposure to tea vegetation through direct ingestion of the leaves may overestimate risk. The results emphasize that food preparation methods must be considered when conducting an HHRA. This study illustrates how collaboration between Western scientists and First Nations communities can add greater clarity to risk assessments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 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 it