Animal control measures and their relationship to the reported incidence of dog bites in urban Canadian municipalities.
Bibliographic record
Abstract
Various measures, including ticketing, licensing, and breed-specific legislation, are used by municipalities to control dog bites, but their effectiveness is largely unknown. Thirty-six urban Canadian municipalities provided information about their animal control practices, resourcing, and (for 22 municipalities) rate of reported dog bites. Municipalities differed widely in rates of licensing (4% to 75%) and ticketing (0.1 to 83 per 10,000 people), even where staffing and budgets were similar. Reported frequency of dog bites ranged from 0 to 9.0 (median 1.9) per 10,000 people. Rates were generally higher in municipalities with higher ticketing, licensing, staffing, and budget levels. However, in municipalities with very active ticketing the reported bite rate was much lower than predicted by a linear regression on ticketing rate (quadratic regression, R(2) = 0.52), likely reflecting a reduction in actual bites with very active enforcement. Municipalities with and without breed-specific legislation did not differ in reported bite rate. Ticketing appeared most effective in reducing dog bites, although it may also lead to increased reporting.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".