Literature review data and code associated with "An updated estimate of the number of birds killed by outdoor cats in Canada" published in Avian Conservation and Ecology
Bibliographic record
Abstract
Supplementary literature review data for the paper "An updated estimate of the number of birds killed by outdoor cats in Canada" published in Avian Conservation and Ecology. Code to reproduce paper's results and figures included.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. Using the best available data at the time, Blancher (2013) estimated that cats 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 – 197 million birds per year in Canada, 71% lower than Blancher’s (2013) 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. While 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.
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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.042 | 0.083 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.307 | 0.059 |
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".