A conceptual framework for habitat use and research priorities for the greater bilby (Macrotis lagotis) in the north of Western Australia
Bibliographic record
Abstract
Little is known of the area of occupancy, extent of occurrence, abundance, density or habitat use of the greater bilby (Macrotis lagotis) in the north of Western Australia. To seek broad collaborative agreement on a research agenda, the Western Australian Department of Parks and Wildlife hosted a workshop where research priorities were identified through a facilitated process. Five key areas for future research effort were identified: (1) refine survey methods, (2) improve understanding of habitat use, (3) improve understanding of the genetic structure of (meta)populations, (4) improve understanding of the threat posed by introduced predators and herbivores, and (5) improve understanding of how fire regimes affect bilby conservation. A conceptual model describing the main landscape components thought to be influencing distribution is used to reconcile existing knowledge, link research priorities for the bilby in the north of Western Australia, and guide the development of an integrated program of research. The broad nature of the priorities reflects the limited knowledge of bilbies in the north of the state; however, this research program provides an opportunity to increase knowledge to enact both species- and ecosystem-focused approaches to conservation, and potentially contributes towards the implementation of more dynamic conservation approaches for mobile species.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".