Contributions and perspectives of Indigenous Peoples to the study of mercury in the Arctic
Bibliographic record
Abstract
Arctic Indigenous Peoples are among the most exposed humans when it comes to foodborne mercury (Hg). In response, Hg monitoring and research have been on-going in the circumpolar Arctic since about 1991; this work has been mainly possible through the involvement of Arctic Indigenous Peoples. The present overview was initially conducted in the context of a broader assessment of Hg research organized by the Arctic Monitoring and Assessment Programme. This article provides examples of Indigenous Peoples' contributions to Hg monitoring and research in the Arctic, and discusses approaches that could be used, and improved upon, when carrying out future activities. Over 40 mercury projects conducted with/by Indigenous Peoples are identified for different circumpolar regions including the U.S., Canada, Greenland, Sweden, Finland, and Russia as well as instances where Indigenous Knowledge contributed to the understanding of Hg contamination in the Arctic. Perspectives and visions of future Hg research as well as recommendations are presented. The establishment of collaborative processes and partnership/co-production approaches with scientists and Indigenous Peoples, using good communication practices and transparency in research activities, are key to the success of research and monitoring activities in the Arctic. Sustainable funding for community-driven monitoring and research programs in Arctic countries would be beneficial and assist in developing more research/monitoring capacity and would promote a more holistic approach to understanding Hg in the Arctic. These activities should be well connected to circumpolar/international initiatives to ensure broader availability of the information and uptake in policy development.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".