SEROPREVALENCE OF ERYSIPELOTHRIX RHUSIOPATHIAE IN BEAUFORT SEA POLAR BEARS (URSUS MARITIMUS) IS LINKED TO RINGED SEAL (PUSA HISPIDA) DEMOGRAPHICS
Bibliographic record
Abstract
Polar bear (Ursus maritimus) life history is intimately associated with the distribution of sea ice and their prey in Arctic ecosystems. These ecosystems are changing in response to climate warming, resulting in the increased prevalence of pathogens in polar bears. Erysipelothrix rhusiopathiae has a long history of infection in domestic species and more recently in wildlife in the Canadian Arctic. As a result of increasing reports of E. rhusiopathiae causing morbidity and mortality in Arctic terrestrial mammals, we tested the seroprevalence of E. rhusiopathiae in Beaufort Sea polar bears sampled in 1985-87, 1992, 1994, and 2003-11. Our sample of 180 polar bears (117 females, 61 males, two unknown) with a median age of 9 yr (range 1-26 yr) had a seropositivity of 27.2% (49/180 individuals). We used binomial logistic regressions to investigate biotic and abiotic factors that may be linked to seropositivity. The resulting top model found that increased predation on adult ringed seals (Pusa [Phoca] hispida) and negative winter Arctic Oscillation Index (AOI) years were associated with a higher probability of seropositivity. Ringed seals may be a reservoir for E. rhusiopathiae via their consumption of infected prey, as the pathogen can persist in marine fish, molluscs, and crustaceans. Negative winter AOIs in our data set reflected high ice volume years, which reduced ringed seal natality, resulting in fewer seal pups available as prey. Our results suggest that exposure to E. rhusiopathiae in Beaufort Sea polar bears is modulated by a predator-prey mechanism.
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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 | 0.000 | 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) | 0.001 | 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".