Cause and effects of a megafire in sedge-heathland in the Tasmanian temperate wilderness
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 World Heritage wilderness of south-western Tasmania contains a complex vegetation mosaic of eucalypt forest, myrtaceous scrub and fire-sensitive rainforest embedded in highly flammable sedge–heathland. Aboriginal burning shaped this temperate region for millennia, and large, severe wildfires have prevailed since European settlement in the early 19th century. In 2013, the Giblin River fire burnt 45 000 ha of wilderness, most of which was sedge-heathland. We surveyed the fire footprint, and an adjacent management burn, to investigate the drivers of fire severity in sedge-heathland and to assess the regeneration response of woody vegetation and how these were influenced by antecedent fire histories. Analyses based on multi-model inference identified time since fire as the most important driver of sedge-heathland fire severity, as measured by diameter of burnt twigs. Mortality was high for both main stems (98%) and whole plants (91%), with only 16% of dead stems resprouting. Resprouting and seedling establishment were little affected by fire severity. The value of prescribed burning in reducing both the extent and severity of wildfires in the south-western Tasmanian landscape, and in maintaining stand-age heterogeneity, is illustrated by the wildfire having self-extinguished on the boundary of the management burn.
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 | 0.001 | 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) | 0.000 | 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 it