Tree use by koalas (Phascolarctos cinereus) after fire in remnant coastal forest
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 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.
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 | 0.003 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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