Survey techniques and impact mitigation for the Endangered northern quoll (Dasyurus hallucatus) in the semi-arid landscapes of the Pilbara
Bibliographic record
Abstract
Improvements in survey techniques for threatened species gives quantifiable confidence about their presence or absence at a given location, enhancing our understanding of patterns of distribution and abundance. This is particularly important for legislatively protected threatened species that may be at risk of disturbance. Survey techniques vary in detection confidence, resource investment, and invasive impacts to individuals. We review the appropriate applications of techniques in surveying for the endangered northern quoll (Dasyurus hallucatus), including the effort required to be 95% confident of detecting presence and monitoring change in population trends in the Pilbara bioregion. The outlined protocols indicate best practice for effective and efficient northern quoll monitoring while protecting the welfare of the animals being monitored, and are relevant to Environmental Protection and Biodiversity Conservation Act requirements. We also provide suggestions to mitigate impacts on animals and habitat, and describe future directions and emerging techniques for the monitoring of northern quolls and other endangered species. This information is targeted at those interested in monitoring northern quolls in a field setting, including researchers, environmental consultants, Traditional Owners, and land managers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".