Bibliographic record
Abstract
Arctic parts of Russia (Siberia), Alaska, Canada, and Greenland are characterized by Indigenous populations living in small isolated settlements where household crowding is high. People living in Svalbard and Antarctica are predominantly Caucasians. The main infectious diseases that occur in excess numbers in Indigenous populations in the Arctic compared with Northern European/American populations include invasive bacterial diseases caused by Streptococcus pneumoniae and Haemophilus influenzae , tuberculosis, chronic otitis media, respiratory tract infections including RSV and influenza, hepatitis B virus infection, sexually transmitted infections, Helicobacter pylori infection, parasitic infections such as trichinellosis, giardiasis and toxoplasmosis, and bacterial zoonoses such as tularemia. Severe acute respiratory syndrome coronavirus 2 (SARSCoV2) impacted many circumpolar regions. However, in Northern Canada and Greenland with relatively younger populations along with stringent public health measures and travel restrictions, severe outcomes were less common compared with Southern Canada and Denmark, respectively. In Alaska, high rates of SARS-CoV-2 were documented among rural Indigenous populations when compared to non-Indigenous populations residing in lower latitude states of the United States. Infectious disease patterns in people living in Svalbard and Antarctica mainly reflect those of their native countries.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.041 |
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".