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
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".