Blood on the Ice: the Need for Culturally Inclusive One Health Surveillance of Anthropozoonoses in the Arctic.
Bibliographic record
Abstract
INTRODUCTION: A zoonotic disease research focus on tropical and temperate climates often overlooks the arctic regions that also host diverse animal-borne pathogens. Indigenous populations in the arctic have close connections with both the land and animals which can put them at risk. Inherent in spiritual, cultural, social, and subsistence activities, time on the land is essential to definitions of health and wellness, a connectedness so elemental that it has been recognized in the UN Declaration on the Rights of Indigenous Peoples (2008). Subsistence hunting, fishing, herding, and butchering of animals takes place in conditions that are suboptimal for the prevention of zoonotic infection. Given the Arctic's small, remote populations and often substandard medical care, cases of infection can be overlooked as a consequence ( Indigenous One Health in the Arctic: A systematic Literature Review of Circumpolar Zoonoses , Nelson et al., 2014). METHODS: The One Health Initiative seeks to build multidisciplinary collaborations for the purposes of controlling zoonotic diseases that include veterinary and medical professionals. By first examining the Initiative through a medical anthropology framework, then subsequently discussing and considering traditional indigenous knowledge sources on animal behavior and human health, an inclusive model that would respect and incorporate elements of both models is developed.
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".