Healthy ecosystems for human and animal health: Science diplomacy for responsible development in the Arctic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate warming is occurring most rapidly in the Arctic, which is both a sentinel and a driver of further global change. Ecosystems and human societies are already affected by warming. Permafrost thaws and species are on the move, bringing pathogens and vectors to virgin areas. During a five-year project, the CLINF – a Nordic Center of Excellence, funded by the Nordic Council of Ministers, has worked with the One Health concept, integrating environmental data with human and animal disease data in predictive models and creating maps of dynamic processes affecting the spread of infectious diseases. It is shown that tularemia outbreaks can be predicted even at a regional level with a manageable level of uncertainty. To decrease uncertainty, rapid development of new and harmonised technologies and databases is needed from currently highly heterogeneous data sources. A major source of uncertainty for the future of contaminants and infectious diseases in the Arctic, however, is associated with which paths the majority of the globe chooses to follow in the future. Diplomacy is one of the most powerful tools Arctic nations have to influence these choices of other nations, supported by Arctic science and One Health approaches that recognise the interconnection between people, animals, plants and their shared environment at the local, regional, national and global levels as essential for achieving a sustainable development for both the Arctic and the globe.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it