Cause and effects of a megafire in sedge-heathland in the Tasmanian temperate wilderness
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".