Adaptations of large marsupials to survival in winter snow cover: locomotion and foraging
Bibliographic record
Abstract
The small extent of seasonally snow-covered Australian mountains means that there has not been a great selective pressure on the mammalian fauna for adaptations to this environment. Only one large marsupial, the common wombat (Vombatus ursinus (Shaw, 1800)), is widespread above the winter snowline. In the past 20 years, with snow depth and duration declining, the swamp wallaby (Wallabia bicolor (Desmarest, 1804)) has become more common above the winter snowline. The red-necked wallaby (Macropus rufogriseus (Desmarest, 1817)) is common in alpine Tasmania where seasonal snow cover is neither as deep nor as long-lasting as on the mainland, but has only been recorded regularly above the winter snowline in the mainland Snowy Mountains since 2011. This study examines morphological aspects of locomotion of these three herbivorous marsupials in snow. The wombat is the best adapted to snow, with quadrupedal gait and an expanded home range allowing it to locate and feed on the same plant groups as it does at lower elevation. Wallabies are poorly adapted to locomotion in snow, but the browsing swamp wallaby is able to maintain its dietary habit by feeding on exposed shrubs in deep snow, whereas the red-necked wallaby, which depends more on grazing, appears constrained to areas where snow is shallow.
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.
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 | 0.000 | 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".