Mapping the likelihood of koalas across New South Wales for use in Private Native Forestry: developing a simple, species distribution model that deals with opportunistic data
Bibliographic record
Abstract
In Private Native Forestry in New South Wales, species-specific provisions in the code of practice are triggered by the presence of koalas (Phascolarctos cinereus), based on existing database records in the Atlas of NSW Wildlife. Whereas Species Distribution Modelling allows questions to be posed regarding the distribution of a species, and how it relates to environmental variables and threats, the key question, in many management situations, is whether or not a species is, or has been, present at a particular location, rather than the overall predicted distribution of the species. This is particularly the case for such a high-profile species as the koala. In this project, we developed a simple distribution model for the koala in New South Wales based on the proportion of koala records from within a suite of mammal records in 10 km × 10 km cells. This provides a measure of the likelihood of koalas being present. At the same time it allows deficiencies in the data to be highlighted, and recommendations made for further survey. This model and map will allow the potential for more robust and transparent decisions to be made regarding koala protection in areas proposed for private native forestry.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".