Perspective: Dimensions of Environment and Health in Arctic Communities
Bibliographic record
Abstract
In the Arctic, environment and health are linked in myriad ways. A key emphasis has been on numerous long-lived contaminants in traditional foods, particularly marine mammals, and their well-documented impacts on human, animal and environmental health ("One health approach"). More recent concerns for Indigenous communities focus on the (side) effects of the switch to a modern, processed diet, which is accompanied by a loss of tradition and emerging health impacts. Furthermore, the availability of traditional foods is increasingly threatened by the impacts of climate change, which also causes the emergence and spread of new and old diseases, such as anthrax. Climate change, including thawing permafrost and new forest fire regimes, threatens the built environment and infrastructure. In particular, well-built, planned, and healthy housing is urgently needed, given that much time is spent indoors. Health care, particularly for remote and Indigenous communities, is sparse, and often ignores traditional knowledge and local languages. Indigenous communities in the Arctic continue to suffer from marginalization, resource colonization/extraction, and the impacts of racism. Recent examples of the green energy transition, such as in Norway, continue a pattern of ignoring Indigenous rights and lifestyles. Overall, the connection between environment and health in the Arctic is multifaceted and complex, and investigations and solutions ought to embrace an interdisciplinary and holistic approach toward improving Environmental and Human Health in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".