Home ranges of feral cats (Felis catus) in central-western New South Wales, Australia
Bibliographic record
Abstract
Twenty-one feral cats were radio-tracked using direct sighting and triangulation techniques (amassing 730 location fixes) during winter in an agricultural landscape in central-western New South Wales. Factors affecting home-range size, home-range overlap and habitat use were assessed. Mean home-range size was 248 ha (s.e. = 34.9, n = 15 cats, 598 location fixes). Home-range size and habitat use were not influenced by sex or age of adult cats, prey abundance or time of day. However, cat weight significantly influenced range size, with heavier cats having larger ranges than smaller cats. Although the cats are apparently solitary, their home ranges overlapped considerably, particularly between young adults and old adult cats. Cats were active both by day and night and did not occupy permanent dens. Home ranges encompassed mixed habitat types that provided both shelter and prey. Open woodland and open forest were the main habitat types covered by home ranges, but within these areas cats showed a preference for grassland, where rabbits were more abundant. The results recorded in this study indicate that cat-control programs should concentrate in mixed habitat areas, where both shelter and food are available, and over widely dispersed areas. The absence of group living suggests that the effectiveness of virally vectored fertility or biological control agents would be limited.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".