Zoonotic pathogens in wild muskoxen ( <i>Ovibos moschatus</i> ) and domestic sheep ( <i>Ovis aries</i> ) from Greenland
Bibliographic record
Abstract
The present study aimed to estimate the prevalence of zoonotic pathogens Giardia duodenalis, Cryptosporidium spp., Toxoplasma gondii and Erysipelothrix in muskoxen (Ovibos moschatus) and sheep (Ovis aries) from Greenland. In 2017 and 2018, faecal samples were collected from wild muskoxen from three distinct populations (Zackenberg, Kangerlussuaq, and Ivittuut) and from domestic sheep from southwest Greenland. Blood samples were collected from muskoxen from Kangerlussuaq and Ivittuut and from sheep. Faecal samples were tested for specific DNA of G. duodenalis and Cryptosporidium spp., and blood samples were tested for antibodies against T. gondii and Erysipelothrix. The estimated prevalence of G. duodenalis was 0% (0/58), 17% (7/41) and 0% (0/55) in muskoxen from Zackenberg, Kangerlussuaq and Ivittuut, respectively, and 37% (16/43) in sheep. The estimated prevalence of Cryptosporidium was 0% (0/58), 2% (1/41), 7% (4/55) in muskoxen from Zackenberg, Kangerlussuaq, Ivittuut, respectively, and 2% (1/43) in sheep. Neither Giardia nor Cryptosporidium were detected in winter samples (0/78). Of the positive samples, Giardia from one muskox sample only was successfully typed as G. duodenalis assemblage A, and Cryptosporidium from two muskoxen was successfully typed as C. parvum, subtype IIdA20G1e. The estimated T. gondii seroprevalence was 2% (1/44) and 0% (0/8) in muskoxen from Kangerlussuaq and Ivittuut, respectively, and 1% (1/155) in sheep. The estimated Erysipelothrix seroprevalence was 2% (1/45) and 13% (1/8) in muskoxen from Kangerlussuaq and Ivittuut, respectively, and 7% (10/150) in sheep. The results of this study add to the scarce knowledge on zoonotic pathogens 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".