Indigenous One Health in the Arctic, a Systematic Literature Review of Circumpolar Zoonoses.
Bibliographic record
Abstract
INTRODUCTION: The PUBMED, EBSCO, and Web of Science databases were utilized to perform a systematic review of research on zoonotic exposure of indigenous populations in the Arctic region, with no publication date limitation. METHODS: Study selection: We included original research studies that evaluated either or both exposures and disease outcomes related to zoonotic pathogens in indigenous communities, focusing in particular on circumpolar communities. Search: We performed literature searches in PUBMED using the following search strategy and MeSH keywords: ‘zoonoses' and ‘indigenous population' or ‘arctic.' We searched PUBMED, EBSCO, and Web of Science using the following search strategy: (‘indigenous' or ) and (‘one health' or ‘zoono*') with or without ( ). All searches were conducted without date restriction. We considered articles in English or Spanish or French. RESULTS: The initial searches resulted in 755 articles. Exclusions were used: research articles only, geographic region only, indigenous only. The resulting 44 articles were identified and reviewed for relevance. After sorting the articles and removing those that were out of the defined circumpolar geographic area, reviews, and/or were inaccessible, the remaining 33 articles that were pertinent to indigenous One Health in the Circumpolar region were reviewed. A critical examination of the aggregated research presented three article themes: specific animal exposure studies, new zoonotic risks due to global climate change, and epidemiological disease tracking.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".