Ecology and movement of urban koalas adjacent to linear infrastructure in coastal south-east Queensland
Bibliographic record
Abstract
In Redland City, koalas (Phascolarctos cinereus) are in rapid decline as they are exposed to anthropogenic threats such as habitat clearing, dog attacks, vehicle collisions and disease. This study investigated the influence of linear infrastructure on the movement and habitat use of urban koalas. Seven koalas were tracked for up to 28 weeks during the breeding season. Home ranges were calculated for 95% Minimum Convex Polygon (MCP95%) and 95% fixed Kernel Density (FK95%). Koalas responded to the landscape in different ways. Linear infrastructure did not restrict the movements of most koalas. Home ranges varied from 1.1 to 31.5 ha MCP95% and from 5 to 55 ha for FK95%. Koalas mainly used Eucalyptus tereticornis throughout the study site. A variety of non-regionally endemic and regionally endemic trees in urban and remnant vegetation areas were used, suggesting that all trees are potentially koala habitat. At the completion of the study, four koalas remained alive, two were killed by trains and one died from a dog attack. Despite the small sample size and short duration, our results suggest that koalas are able to navigate linear infrastructure; however, the high rates of mortality associated with these movements puts the long-term viability of urban koala populations in doubt.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".