The diet of the dingo (Canis lupus dingo and hybrids) in north-eastern Australia: a supplement to the paper of Brook and Kutt (2011)
Bibliographic record
Abstract
Dingoes and other wild dogs (Canis lupus dingo and hybrids) are generalist predators that consume a wide variety of different prey species within their range. Little is known, however, of the diets of dingoes in north-eastern Australia where the potential for impacts by dingoes exists. Recently new information has been provided on the diets of dingoes from several sites in Queensland, Australia, significantly adding to the body of published knowledge on ecosystems within this region. Further information on the diet of dingoes in north-eastern Australia is added from 1460 scats collected from five sites, representing tropical savannahs, tropical offshore islands (and a matched mainland area), dry sclerophyll forests and peri-urban areas on the fringe of Townsville. Macropods, possums and bandicoots were found to be common prey for dingoes in these areas. Evidence suggested that the frequency of prey remains in scats can be an unreliable indicator of predation risk to potential prey and it was found that novel and unexpected prey species appear in dingo diets as preferred prey become unavailable. The results support the generalisation that dingoes prefer medium- to large-sized native prey species when available but also highlight the capacity for dingoes to exploit populations of both large and small prey species that might not initially be considered at risk from predation based solely on data on scats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".