A One Health Approach Addressing Dog Overpopulation in Northern Canadian Communities
Bibliographic record
Abstract
Dog overpopulation in northern Canadian communities is a major health concern affecting humans, non-human animals, and the environment. Issues include aggression between dogs, contamination of soil and water systems, and a heightened risk of injury or zoonotic disease spread (Boissonneault & Epp, 2018; Brook et al., 2010). These One Health concerns are worsened by barriers in northern Canadian communities including isolation from veterinary or medical services, high cost, and potential judgement over the treatment of companion animals (CBC News, 2018). Several grassroots organizations across Canada have developed initiatives to address aspects of the dog overpopulation problem. Despite these efforts, there are very few sustainable, long-term interventions targeting isolated northern Canadian communities such as Inuvik, Northwest Territories. The proposed initiative therefore aims to fill this gap, reducing the dog overpopulation problem over time by partnering with organizations to provide free-roaming or stray dogs with a chemical contraceptive. It also aims to raise awareness across Canada and draw in donations to fund these procedures using the “Sponsor-A-Dog” approach. This may reduce the effects of dog overpopulation on humans, non-human animals, and the environment, with potential for expansion to other northern communities or canine-related health concerns.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".