Estimating spatial and temporal trends of dog importation into Canada from 2013 to 2019.
Bibliographic record
Abstract
Background and objective: For several years, there has been growing concern over the public and animal health impacts of dog importation, with many Canadian veterinarians reporting increasing diagnoses of exotic pests and pathogens. This study is the first to estimate the number of dogs imported into Canada and describe spatial and temporal trends. Animal and procedure: Commercial and a subset of personal dog importation records, obtained from the Canada Border Services Agency, were used to estimate the total number of dogs imported into Canada from 2013 to 2019. Results: The number of dogs imported annually increased by > 400% over the study period, with > 37 000 dogs imported in 2019. The majority of dogs (72%) were imported from the United States and Eastern Europe, and 23% originated in a country considered high-risk for canine rabies. Conclusion: Dog importation into Canada has increased substantially over time. Moving forward, education and improved tracking will be essential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".