Methodologies and challenges in Arctic human health risk assessment: case studies and evaluation of current practices
Bibliographic record
Abstract
In Arctic populations, a primary route of exposure to contaminants is through the diet. The health risks associated with these exposures can be characterised by conducting human health risk assessments. However, while there is guidance from many international and national organisations, there are limited examples of human health risk assessment in the Arctic. The 2022 AMAP Human Health Assessment Report was the first AMAP report to describe, in one place, the utility of food-based, dietary intake-based and human tissue-based contaminant data in estimating risk. Here, we present available tools, case studies and challenges associated with conducting human health risk assessments in the Arctic. Future efforts in the Arctic should be able to use this information to best interpret human exposure to contaminants in a risk-based context.
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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 | 0.231 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".