The potential impact of climate change on infectious diseases of Arctic fauna
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.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".