Combining a map-based public survey with an estimation of site occupancy to determine the recent and changing distribution of the koala in New South Wales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study demonstrates one solution to a problem faced by managers of species of conservation concern – how to develop broad-scale maps of populations, within known general distribution limits, for the purpose of targeted management action. We aimed to map the current populations of the koala, Phascolarctos cinereus, in New South Wales, Australia. This cryptic animal is widespread, although patchily distributed. It principally occurs on private property, and it can be hard to detect. We combined a map-based mail survey of rural and outer-urban New South Wales with recent developments in estimating site occupancy and species-detection parameters to determine the current (2006) distribution of the koala throughout New South Wales. We were able to define the distribution of koalas in New South Wales at a level commensurate with previous community and field surveys. Comparison with a 1986 survey provided an indication of changes in relative koala density across the state. The 2006 distribution map allows for local and state plans, including the 2008 New South Wales Koala Recovery Plan, to be more effectively implemented. The application of this combined technique can now be extended to a suite of other iconic species or species that are easily recognised by the public.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it