The diet of the common wombat (Vombatus ursinus) above the winter snowline in the decade following a wildfire
Bibliographic record
Abstract
The use of high elevations with deep snow cover presents a challenge to mammalian herbivores, which is exacerbated by subalpine vegetation dynamics such as slow regrowth following disturbance. We postulated that post-fire responses of common wombats (Vombatus ursinus) at high elevations would differ from those at low elevations. We examined the winter diet of common wombats in the Snowy Mountains in the decade after fire in burnt and unburnt areas and compared our results to published diet studies from low elevations. Optimal foraging theory predicts that as food resources become scarce herbivores respond by widening their choice of foods, yet we found that wombats have only marginally wider dietary breadth at higher than at lower elevations in terms of plant forms and diet breadth in terms of species was not greater. The use of shrubs and the tall herb Dianella tasmanica enables wombats to reduce the energetic costs of digging for food in snow. Able to survive fire in a burrow, the wombat is then capable of responding to reduced foraging opportunities following fire by broadening the range of species consumed and adopting foraging strategies that exploit temporally improved food quality, demonstrated by the greater proportion of grass consumed in burnt sites.
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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 | 0.000 | 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".