Estimating feral cat densities using distance sampling in an urban environment
Bibliographic record
Abstract
Abstract Synthesis and applications Estimating feral cat population densities in urban environments can be difficult due to lack of public space and human interference. The purpose of this study was to use distance sampling in a citywide landscape to determine population size and areas of high abundance to inform trap–neuter–release management. Line transect distance sampling was used to estimate density of the feral cat population in Windsor, Ontario from June to July 2014. Windsor has a human population of 217,188 and is about 146 km 2 in size. Most transects were placed along local roads. Density was estimated at about 13.3 (95% CI 9.7–18.1) cats per km 2 , and an estimated population size of 1,858 cats (95% CI 1,361–2,537) cats with the highest relative density occurring in West and Central Windsor. Urban wildlife managers could utilize these methods to monitor feral cat populations and evaluate the effectiveness of trap–neuter–release programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".