The geographic distribution, and the biotic and abiotic predictors of select zoonotic pathogen detections in Canadian polar bears
Bibliographic record
Abstract
Increasing Arctic temperatures are facilitating the northward expansion of more southerly hosts, vectors, and pathogens, exposing naïve populations to pathogens not typical at northern latitudes. To understand such rapidly changing host-pathogen dynamics, we need sensitive and robust surveillance tools. Here, we use a novel multiplexed magnetic-capture and droplet digital PCR (ddPCR) tool to assess a sentinel Arctic species, the polar bear (Ursus maritimus; n = 68), for the presence of five zoonotic pathogens (Erysipelothrix rhusiopathiae, Francisella tularensis, Mycobacterium tuberculosis complex, Toxoplasma gondii and Trichinella spp.), and observe associations between pathogen presence and biotic and abiotic predictors. We made two novel detections: the first detection of a Mycobacterium tuberculosis complex member in Arctic wildlife and the first of E. rhusiopathiae in a polar bear. We found a prevalence of 37% for E. rhusiopathiae, 16% for F. tularensis, 29% for Mycobacterium tuberculosis complex, 18% for T. gondii, and 75% for Trichinella spp. We also identify associations with bear age (Trichinella spp.), harvest season (F. tularensis and MTBC), and human settlements (E. rhusiopathiae, F. tularensis, MTBC, and Trichinella spp.). We demonstrate that monitoring a sentinel species, the polar bear, could be a powerful tool in disease surveillance and highlight the need to better characterize pathogen distributions and diversity in the Arctic.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".