An updated estimate of the number of birds killed by outdoor cats in Canada
Bibliographic record
Abstract
Domestic cats, Felis catus, can be found almost everywhere in the world and estimating their impact on wildlife, including birds, requires the most up-to-date information. There are an estimated 9.3 million pet cats in Canada, 30–60% of which are given unrestricted access to the outdoors. With the best available data in 2013, cats were estimated to kill between 105–348 million birds per year in Canada, making them the leading measurable cause of bird mortality in the country. However, a decade later, research on outdoor cats and their predation of birds has increased considerably, providing an opportunity to revisit this mortality estimate. Using recent data on predation rates and cat abundance, we estimated that cats kill between 19 and 197 million birds per year in Canada, 71% lower than the earlier estimate. This does not mean that cat populations or predation rates on birds have declined since the previous estimate. Rather, we suggest that the difference can be primarily attributed to lower outdoor cat abundance estimated from field surveys compared to previously used cat ownership surveys and media reports of shelter intake data. Although the estimated number of birds killed annually by cats is considerably lower than the previous estimate, outdoor cats remain a serious concern for native bird populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".