Climate change, parasites and shifting boundaries
Bibliographic record
Abstract
Background Around the world the three major components of climate change already evident and escalating in magnitude and significance are; 1) warming; 2) altered patterns of precipitation; and 3) an increased incidence of extreme climatic events [1]. For the structure and function of ecosystems, impacts of climate change vary with place and with time, and among the key outcomes are shifting boundaries for many components and processes within the systems. Among these components are pathogens and infectious diseases, including those caused by helminth, arthropod and protozoan parasites in people, domestic animals, and wildlife [2]. For host-parasite assemblages, boundaries potentially vulnerable to climate change include those for spatial and temporal distributions of hosts and parasites, for parasite survival and development in hosts and in the environment, for risks of transmission to hosts at critical points in parasite webs, and for health effects on hosts, including the emergence or resurgence of disease. The often complex and obscure linkages and inter-relationships among components of an ecosystem, coupled with the uncertain and variable trajectories for climate change, make it difficult to identify all these vulnerabilities, particularly in the medium to long term. Also, faced with non-overwhelming “stress” most ecosystems display a degree of resilience that may mitigate some of the consequences of climate change [3,4], and in some circumstances the significance of parasites remains essentially unchanged. Finally, some recent shifts in disease occurrence that intuition might suggest are associated with climate change have proved likely to be wholly or partly the result of other factors [5,6]. The primary aim of this paper is to provide a framework for thinking about the critical potential connections between climate change, parasites, people, and wildlife in the circumpolar North, and between these host groups, climate change, parasites and domestic animals in other areas of the world.
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 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 | 0.001 | 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 | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".