Research with Arctic peoples: Unique research opportunities in heart, lung, blood and sleep disorders
Bibliographic record
Abstract
Arctic peoples are spread over eight countries and comprise 3.74 million residents, of whom 9% are indigenous. The Arctic countries include Canada, Finland, Greenland (Denmark), Iceland, Norway, Russia, Sweden and the United States. Although Arctic peoples are very diverse, there are a variety of environmental and health issues that are unique to the Arctic regions, and research exploring these issues offers significant opportunities, as well as challenges. On July 28-29, 2004, the National Heart, Lung, and Blood Institute and the Canadian Institutes of Health Research co-sponsored a working group entitled "Research with Arctic Peoples: Unique Research Opportunities in Heart, Lung, Blood and Sleep Disorders". The meeting was international in scope with investigators from Greenland, Iceland and Russia, as well as Canada and the United States. Multiple health agencies from Canada and the United States sent representatives. Also attending were representatives from the International Union for Circumpolar Health (IUCH) and the National Indian Health Board. The working group developed a set of ten recommendations related to research opportunities in heart, lung, blood and sleep disorders; obstacles and solutions to research implementation; and ways to facilitate international comparisons. These recommendations are expected to serve as an agenda for future research.
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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 | 0.095 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| 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".