The use of point-of-view cameras (Kittycams) to quantify predation by colony cats (Felis catus) on wildlife
Bibliographic record
Abstract
Context Domestic cats (Felis catus) are efficient and abundant non-native predators, recently labelled as primary contributors to global biodiversity loss. Aims Specific research goals included determining the proportion of hunters, estimating hunting efficiency, identifying primary prey and examining predictors of kill rate and efficacy. Methods We investigated hunting of wildlife by stray cats living in managed outdoor colonies on a barrier island in the southeastern USA, and monitored 29 stray cats seasonally in 2014 and 2015 using Kittycam video cameras. Key results In total, 24 cats exhibited hunting behaviour and 18 captured prey. The estimated average daily predation rate from these successful hunters was 6.15 kills per 24-h period. Hunting effectiveness (percentage of capture attempts that translate to a kill) was an average of 44%. The most common type of prey captured was invertebrate (primarily Orthopteran and Hemipteran insects), followed by amphibians and reptiles. Eighty-three percent of kills occurred between dusk and dawn. Conclusions Colony location (near undeveloped island habitat) was related to higher kill rates. Cat sex and nocturnal hunting activity were related to greater hunting efficiency. Implications These results address the significant gap in knowledge about stray cat hunting activities, and raise conservation concerns for some groups of organisms (reptiles and amphibians) that have not been widely identified as vulnerable to cat predation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".