Geographic Transitions of Domestic Cats in Urban Areas through Animal Adoption Centers and the Implications for Population Dynamics
Bibliographic record
Abstract
Animal shelters address animal welfare in communities through the intake and outcome of companion animals, but these efforts affect population dynamics of companion animals based on the distance animals are moved and the factors that underlie intake and outcome. Using data from an animal shelter in Washington, DC we analyzed cat intakes and outcomes based on geographic and socioeconomic factors. Most intakes were stray cats (59%) and cats relinquished by owners (38%) and most outcomes were adoptions (84%). The highest number of intakes were in high development, low-income neighborhoods, whereas the lowest number of intakes were in low development, high-income neighborhoods. The highest number of outcomes were to high-income neighborhoods and there was a trend toward more outcomes in neighborhoods further from the shelter. Cats returned to the shelter were more likely to originate from areas near the shelter whereas cats that were relinquished originated from areas further from the shelter. Stray intakes were less common, and returns to shelter were more common, in high-income, high development areas. Seized cats originated from low-income neighborhoods. Relative to adoptions, the proportion of returned to owner outcomes was higher in low-income neighborhoods that were closer to the shelter as well as high-income neighborhoods that were distant from the shelter. Our results highlight the factors underlying cat intakes and outcomes in shelters that ultimately determine where, when, and how animals are moved across one urban area; these factors must be considered when developing cat population management plans to reach animal welfare and societal goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".