Bibliographic record
Abstract
Medical practice in Churchill is a challenging and rewarding form of wilderness medicine. If any reader is interested in learning more about Churchill or would like to consider visiting or even taking a health care position here, the following resources and contacts are available: The website for Churchill Independent Travel (www.creighton.edu/hutchens/church_travel.html) gives a complete list of options for those wishing to plan their own trip. The Arctic Medicine Conference is a medical conference with talks in Zoonoses, Dentistry, Entomology, and Travel Medicine hosted by Wilderness Medicine in Manitoba (www.skylarkmedicalclinic.com). The JA Hildes Northern Medical Unit is responsible for providing qualified physicians and nurse practitioners to staff northern Manitoba. For those who are interested, please contact Michelle Vandenbroeck at 1(204)789-3711 for information in working in Churchill or other parts of the Hudson Bay Inuit communities (www.umanitoba.ca/faculties/medicine/units/ northern_medical_unit). 1,000 Places to See Before You Die, by Patricia Schultz, is a recent bestseller that gives information about polar bear safaris.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.010 |
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".