Seasonal and spatial overlap in activity between domestic dogs and dingoes in remote Indigenous communities of northern Australia
Bibliographic record
Abstract
Free-roaming domestic dogs in Indigenous communities of northern Australia have the potential to spread diseases at the wild-domestic dog interface. Hunting activities with domestic dogs, commonly practiced in Indigenous communities, also create opportunities for wild-domestic dog interactions in the bush, providing pathways for potential disease spread. Data from a camera-trap study conducted in remote Indigenous communities of northern Australia were used to explore spatial and seasonal opportunities for interactions between dingoes and unsupervised domestic dogs. For each type of dog, activity indices, based on detection events per camera station with an adjustment for sampling effort, were mapped across the study area and plotted against distance to communities. Unsupervised domestic dogs were mostly active in proximity (<1 km) to the communities. However, there was a noticeable peak of activity further in the bush away from the communities, especially in the wet season, coinciding with areas commonly used for hunting activities. In contrast, the activity of dingoes was more homogeneous within the study area, with a higher peak of activity around the communities during the dry season, and in bush areas distant (>10 km) to communities during the wet season. Overall, our findings suggest that interactions between dingoes and unsupervised community dogs are more likely to occur around the communities, particularly during the dry season, whereas in the wet season, there is increased opportunity for interactions in distant areas in the bush between dingoes and, presumably, hunting dogs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".