FEASIBILITY OF USING COYOTES (CANIS LATRANS) AS SENTINELS FOR BOVINE MYCOBACTERIOSIS (MYCOBACTERIUM BOVIS) INFECTION IN WILD CERVIDS IN AND AROUND RIDING MOUNTAIN NATIONAL PARK, MANITOBA, CANADA
Bibliographic record
Abstract
Elk (Cervus elaphus manitobensis) and white-tailed deer (Odocoileus virginianus) in the Riding Mountain National Park (RMNP) region of southwestern Manitoba have been identified as a likely wildlife reservoir of Mycobacterium bovis, the causative agent of bovine mycobacteriosis in livestock. The feasibility of using coyotes (Canis latrans) collected from trappers as a sentinel species was investigated. Retropharyngeal, mesenteric, and colonic lymph nodes and tonsils collected at necropsy from 82 coyotes were examined by bacterial culture, polymerase chain reaction (PCR), and acid-fast histopathology. Mycobacterium bovis was not identified in any animal by culture or PCR although Mycobacterium avium species were isolated. A single acid-fast organism was identified on histopathologic examination of one animal. Based on the methods used in this study, trapper-caught coyotes do not appear to be a sensitive sentinel species of M. bovis infection in cervids in and around RMNP.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".