Arctic Vulnerability: Examining Biosecurity Risks Amidst Climate Change
Bibliographic record
Abstract
Abstract Building on the objectives of a working paper, this chapter explores the intricate relationship between the Arctic Circle, climate change, and One Health. The Arctic is exceptionally susceptible to climate change, warming three times faster than the global average, leading to increased melting of snow, ice sheets, and permafrost. These changes heighten the vulnerability of flora, fauna, and Indigenous communities that thrive in this ecosystem. Additionally, thawing permafrost releases numerous toxins and revives dormant microorganisms, increasing biosecurity risks to human, animal, and plant health. Urgent enhancement of health surveillance is essential to identify and contain potential zoonotic disease outbreaks promptly. Informed by the One Health approach which emphasises the interconnection of environmental, animal, and human well-being, this working paper aims to enrich existing literature by systems mapping diverse One Health surveillance systems in this region. The overarching goal is to improve public health outcomes in the Arctic Circle by fostering transdisciplinary collaborations and addressing challenges associated with implementing the One Health framework in this vast and unique landscape. Aligned with the United Nations 2030 Agenda for Sustainable Development, emphasising transformative actions for planet protection, this paper advocates for the successful integration of the One Health framework to improve the holistic health of the ecosystem. In doing so, it supports ecological education and contributes to the overall goal of safeguarding the planet.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".