Molecular Epidemiological study of Campylobacter spp. Carriage in Mammalian Wildlife and Livestock on Southern Ontario
Bibliographic record
Abstract
This thesis is focuses on Campylobacter carriage in mammalian wildlife and livestock in southern Ontario. Multi-level logistic regression models were constructed to investigate Campylobacter spp. and antimicrobial resistant Campylobacter spp. carriage in wildlife and livestock species on 25 farms. Samples were collected from dairy and beef cattle, swine and raccoons as well as a selection of other mammalian wildlife. Molecular subtyping data, produced by the Campylobacter–specific 40-gene comparative genomic fingerprinting assay (CGF40), were used to compare isolates from wildlife and livestock. Cluster analysis was conducted to visualize the groupings of wildlife and livestock C. jejuni isolates found. Wildlife and livestock carried Campylobacter at significantly different prevalences, had different antibiograms, and rarely shared the same CGF40 subtypes. Dendrogram and correspondence analysis indicated that the subtypes of Campylobacter circulating in livestock and wildlife populations were distinct. Combined, these results suggest that transmission between mammalian wildlife, especially raccoons, and livestock is limited.
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 | 0.001 |
| 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 | 0.000 |
| 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".