Conducting community-led research using trail cameras to develop baseline wandering domestic cat local abundance estimates
Bibliographic record
Abstract
Assessing cat local abundance provides information on where wandering cat numbers are highest and what habitats or factors are associated with wandering cats. A variety of stakeholders can lead this research and then use the findings to make scientifically informed decisions to guide the physical locations of cat management actions. Here, we document a framework that engages community members, uses minimal equipment (six trail cameras), and provides scientifically derived information for interested parties to inform, direct, or test the effectiveness of cat management practices. Using these methods in Gatineau, Quebec, Canada, we demonstrate how we estimated cat population size and cat local abundances across a variety of co-variates while accounting for non-perfect detection by using 55 trail camera sites and N-mixture models. Urban areas had three-fold higher local wandering cat abundances than parkland areas, and neighbourhoods below the median income had the highest local abundances of wandering cats. We estimated there are between 8905 and 48,419 (mean 21,298) wandering cats in Gatineau, with 18%–73% of those cats being unowned. These findings can be used to identify locations for future cat management. If estimates of cat abundance are repeated, they can assess the effectiveness of management actions.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 0.001 |
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".