Ecological correlates of pneumonia epizootics in bighorn sheep herds
Bibliographic record
Abstract
Bighorn sheep (Ovis canadensis) populations commonly experience pneumonia outbreaks caused by Pasteurella spp. that result in a partial or complete dieoff. Although several factors can contribute to Pasteurella spp. transmission or infectivity in bighorn sheep, to date the importance of such factors in population declines has not been rigorously examined. We evaluated the relationship between pneumonia-induced dieoffs in bighorn sheep and environmental and biological factors by analyzing demographic information for 99 herds across the species' geographic range. Our analysis revealed that 88% of pneumonia-induced dieoffs occurred at or within 3 years of peak population numbers, which implies that density-dependent forces such as food shortage or stress contribute to bighorns' susceptibility to pneumonia. There were few differences in the growth rates of dieoff and non-dieoff populations, suggesting that pneumonia did not manifest itself demographically prior to an outbreak. On average, abundance of lambs was most dramatically reduced post outbreak (66%) relative to that of either rams (35%) or ewes (42%). Deviations in normal precipitation and temperature regimes were not associated with the onset of pneumonia outbreaks, but herds found in proximity to domestic sheep tended to be more susceptible to dieoff. Our results suggest that bighorn sheep herds are rendered vulnerable to pneumonia principally through density-dependent factors, as well as through horizontal transmission of Pasteurella spp. from domestic sheep serving as reservoir hosts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".