Hunting practices in northern Australia and their implication for disease transmission between community dogs and wild dogs
Bibliographic record
Abstract
OBJECTIVE: This survey aimed to understand hunting practices involving domestic dogs in remote Indigenous communities in northern Australia and, in the context of disease transmission, describe the domestic-wild dog interface and intercommunity interactions of hunting dogs during hunting activities. METHODS: A cross-sectional survey of 13 hunters from communities of the Northern Peninsula Area (NPA) of Queensland gathered information on demographics of hunters and hunting dogs, hunting practices and past encounters with wild dogs during hunting trips. Social networks that described the connections of hunters between NPA communities from hunting expeditions were developed. RESULTS: Most hunters interviewed were not aware of any diseases that could be transmitted to dogs (n = 11) or humans (n = 9) from wild animals while hunting. More than half (n = 7) of the respondents had experienced at least one wild dog encounter during hunting in the year prior to the interview. A map of the relative risk of interactions between wild and hunting dogs during hunting trips allowed the identification of high-risk areas in the NPA; these areas are characterised by dense rainforests. The social networks at the community level resulted in relatively large density measures reflecting a high level of intercommunity connectedness. CONCLUSIONS: This study contributes to our knowledge of Australian Indigenous hunting practices and supports the potential for disease transmission at the domestic-wild dog interface and intercommunity level through contacts between hunting dogs during hunting activities. Insights from this study also highlight the need for educational programs on disease management in Indigenous communities of northern Australia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".