Mechanistic modeling of persistent organic pollutant exposure among indigenous Arctic populations: motivations, challenges, and benefits
Bibliographic record
Abstract
Indigenous Arctic populations experience elevated exposures to many environmental contaminants compared with groups residing in southern Canada. This is largely due to consumption of traditional foods, some of which (ringed seals, beluga whales, narwhals, etc.) have relatively high concentrations of persistent organic pollutants. Models of contaminant fate, transport, and bioaccumulation represent powerful tools to explore this exposure issue, wherein combined models can be used to mechanistically and dynamically describe the entire sequence of events linking chemical emissions into the environment to ultimate contaminant concentrations in indigenous Arctic populations. In this review, various approaches adapted and applied to understanding indigenous Arctic contaminant exposure are explored, including early models describing body burdens in single traditional food species to more recent approaches holistically examining uptake and bioaccumulation in entire food chains. The applications of these models are also discussed, including attempts to (i) identify chemical properties favouring transport to, and bioaccumulation in, the Arctic; (ii) clarify the main determinants of temporal trends observed in indigenous Arctic biomonitoring; (iii) explore the impacts of permanent and temporary dietary transitions on current and future indigenous Arctic contaminant exposures; and (iv) correlate modeled early-life pollutant exposures with measured health impacts. The review demonstrates the effectiveness of mechanistic model approaches in investigating indigenous Arctic contaminant exposure, and confirms their utility in continued improvements to understanding associated risk in this unique population 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 | 0.002 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 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".