Update on the International Network for Circumpolar Health Research (INCHR)
Bibliographic record
Abstract
INCHR concluded a very successful second annual general meeting held at the Banff Centre in the Alpine resort town of Banff, Alberta, Canada. It was part of a two-day event which also featured other conjoint meetings including the International Network for Circumpolar Health Association, the International Union of Circumpolar Health, the Canadian Society for Circumpolar Health, and the Arctic Human Health Initiative. There were two concurrent scientific sessions. Members of the CIHR Team in Circumpolar Chronic Disease Prevention outlined various current and planned activities in this 5-year program of research [see announcement in IJCH 2006;65(3):273]. The recently formed IUCH Working Group in Women’s Health also held its inaugural meeting with presentations and discussions of future plans. Overall, some 70 participants were registered, coming from Canada, Denmark, Finland, Greenland, Norway, Russia, Sweden and the United States. Amidst breathtaking scenery of snow capped mountain peaks, participants were able to take full advantage of the opportunities for networking and collegial discussions.(International Journal of Circumpolar Health 66:3 2007)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".