Bibliographic record
Abstract
Important drivers for emergence of infectious disease in wildlife include changes in the environment, shrinking habitats or concentration of wildlife, and movement of people, animals, pathogens, or vectors. In this paper we present three case-studies of emerging parasitic infections and diseases in ungulates in the Canadian north. First we discuss climate warming as an important driver for the emergence of disease associated with Umingmakstrongylus pallikuukensis, a nematode lungworm of muskoxen. Then we examine how Protostrongylus stilesi, the sheep lungworm, emerged (or re-emerged) in muskoxen after re-introduction of this host into its historical range made it sympatric with Dall's sheep. Finally, we consider Teladorsagia boreoarcticus, a newly described and common abomasal nematode of muskoxen that is emerging as a disease-causing parasite and may be an important regulator for muskox populations on Banks Island, Northwest Territories. These and other arctic host-parasite systems are exquisitely tuned and constrained by a harsh and highly seasonal environment. The dynamics of these systems will be impacted by climate change and other ecological disruptions. Baseline knowledge of parasite biodiversity and parasite and host ecology, together with predictive models and long-term monitoring programs, are essential for anticipating and detecting altered patterns of host range, geographic distribution, and the emergence of parasitic infections and 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".