The potential impact of climate change on infectious diseases of Arctic fauna
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
Climate change is already affecting Arctic species including infectious disease agents and greater changes are expected. Some infectious diseases are already increasing but future changes are difficult to predict because of the complexity of host-agent-environment relationships. However mechanisms related to climate change that will influence disease patterns are understood. Warmer temperatures will benefit free living bacteria and parasites whose survival and development is limited by temperature. Warmer temperatures could promote survivability, shorter development rates and transmission. Insects such as mosquitoes and ticks that transmit disease agents may also benefit from climate change as well as the diseases they spread. Climate change will have significant impacts on biodiversity. Disease agents of species that benefit from warming will likely become more prevalent. Host species stressed by changing environmental conditions may be more vulnerable to disease agents. Warming could lead to increased agriculture and other economic opportunities in the Arctic bringing people, domestic food animals, pets and invasive species and their disease agents into Northern regions. Climate warming may also favor the release of persistent environmental pollutants some of which can affect the immune system and may favor increased rates of some diseases.
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 | 0.000 | 0.000 |
| 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