Tree use by koalas (Phascolarctos cinereus) after fire in remnant coastal forest
Bibliographic record
Abstract
The aim of this study was to examine the effects of fire on resource use by a population of koalas in remnant coastal forest. Fifty-five koalas were monitored regularly by radio-tracking for up to 35 months. The attributes of each tree in which the koala was sighted were recorded, giving a total of 8390 records. Analyses were undertaken on a range of ecological information. Regeneration of the forest began immediately following the fires and within three months koalas were seen among the epicormic growth. From a total 4631 trees used by koalas, 3247 (70%) were burnt. Observations of koalas feeding included 53% in burnt trees. Koalas changed trees frequently; individual trees were used once only on 3555 occasions (42% of all observations). Of all the trees used, 95% were used by only one collared koala; no trees were used by more than three koalas. Swamp mahogany (Eucalyptus robusta) was the tree species most frequently used by koalas, particularly at night and by breeding females. Koalas preferred trees of larger diameter (>30 cm) and used significantly taller trees during summer. This study has shown that resource depletion from intense wildfire is short-term for koalas because they utilise burnt trees within months of the fire for both food and shelter.
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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 | 0.000 | 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) | 0.001 | 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".