Intestinal parasites of gray wolves (<i>Canis lupus</i>) in northern and western Canada
Bibliographic record
Abstract
Gray wolves (Canis lupus L., 1758) are mobile opportunistic predators that can be infected by a wide range of parasites, with many acquired via predator–prey relationships. Historically, many of these parasites were identified only to genus or family, but genetic tools now enable identification of parasite fauna to species and beyond. We examined 191 intestines from wolves harvested for other purposes from regions in the Northwest Territories, British Columbia, Saskatchewan, and Manitoba. Adult helminths were collected from intestinal contents for morphological and molecular identification, and for a subset of wolves, fecal samples were also analyzed to detect helminth eggs and protozoan (oo)cysts. Using both detection methods, we found that 83% of 191 intestines contained one or more parasite species, including cestodes (Taenia spp., Echinococcus spp., and Diphyllobothrium sp.), nematodes (Uncinaria stenocephala Railliet, 1884, Trichuris spp., Physaloptera spp., and Toxascaris leonina (von Linstow, 1902)), a trematode (Alaria sp.), and protozoa (Sarcocystis spp., Giardia sp., and Cryptosporidium spp.). Molecular characterization identified one species of Diphyllobothrium (Diphyllobothrium latum (L., 1758) Cobbold, 1858), three species of Taenia (Taenia krabbei Moniez, 1879, Taenia hydatigena Pallas, 1766, and Taenia multiceps Leske, 1786), and two Giardia duodenalis (Davaine) Deschiens, 1921 assemblages (B and C). These results demonstrate the diverse diet of wolves and illustrate the possibility of parasite spillover among wildlife, domestic animals, and people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".