A Scoping Review of Dingo and Wild-Living Dog Ecology and Biology in Australia to Inform Parameterisation for Disease Spread Modelling
Bibliographic record
Abstract
Background: Dingoes and wild-living dogs in Australia, which include feral domestic dogs and dingo-dog hybrids, play a role as reservoirs of disease. In the case of an exotic disease incursion such as rabies these reservoirs could be a threat to the health of humans, domestic animals and other wildlife in Australia. Disease spread models are needed to explore this impact and develop mitigation strategies for responding to an incursion. Our study aim was to describe relevant information from the literature, using a scoping review, on specific topics related to dingo and wild-living dog ecology and biology (topics of interest) in Australia to inform parameterisation of disease spread modelling and identify major research gaps. Methods: A broad electronic search was conducted in five bibliographic databases and grey literature. Two levels of screening and two levels of data extraction were each performed independently by two reviewers. Data extracted included topics of interest investigated, type of population sampled, the presence of lethal control, type of environment, years of collection and GPS coordinates of study sites. Results: From 1666 records captured, the screening process yielded 229 individual studies published between 1862 and 2016. The most frequently reported topics of interest in studies were index of abundance (n=93) and diet (n=68). Among the three key parameters in disease spread modelling (i.e. density, contacts and home range), data on density and contacts were identified as major research gaps in the literature due to the small number of recent studies on these topics and the scarcity of quantitative estimates. The research reviewed was mostly located around central Australia and the east coast, including a few studies on density, contacts and home range. Data from these regions could potentially be used to inform parameterisation for disease spread modelling of dingoes and wild-living dogs. However, the number of studies is limited in equatorial and tropical climate zones of northern Australia, which is a high-risk area for a rabies incursion. Conclusions: Research in northern regions of Australia, especially to generate data regarding density, contacts and home ranges, should be prioritized for future research on dingoes and wild-living 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 | 0.026 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".