Home range of the spotted-tailed quoll (Dasyurus maculatus), a marsupial carnivore, in a rainshadow woodland
Bibliographic record
Abstract
The home ranges, movement patterns and spatial organisation of spotted-tailed quolls (Dasyurus maculatus maculatus) were studied in rainshadow woodland in southern New South Wales, Australia. Fourteen individuals were radio-collared and simultaneously tracked. Home-range size estimates ranged from 621 ha to at least 2561 ha for males, and 88 ha to at least 653 ha for females. Mean home-range size was significantly greater for males (992 ± 276 ha) than females (244 ± 72 ha). The maximum straight-line distances between successive fixes for males over 24-h and 48-h periods were 2529 and 4430 m, respectively, while for females these distances were 1865 and 3085 m. Mean maximum straight-line distances between successive fixes for males over a 24-h period were not significantly different from mean maximum straight-line distances of females (1493 ± 918 v. 1034 ± 540 m). However, over 48 h, the mean maximum distances between successive fixes for males was greater than that of females (2911 ± 934 v. 1680 ± 918 m). The home ranges of males mostly overlapped with those of other individuals, regardless of sex. In contrast, home ranges of females tended not to overlap with those of other females, suggesting some form of spatial separation. Home-range estimates derived for spotted-tailed quolls in our rainshadow woodland study site are comparable to those obtained for the species in wetter vegetation types. From a conservation perspective, it seems that habitat structure and the availability of prey is more important than rainfall or vegetation type in determining spatial requirements of the species. Until more advanced telemetry systems are developed, caution should be applied when using current home-range data on the species to infer breeding systems and patterns of spatial organisation, particularly the issue of territoriality among female quolls.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".