Impact of high-severity fire in a Tasmanian dry eucalypt forest
Bibliographic record
Abstract
Dry eucalypt forests are believed to be highly fire tolerant, but their response to fire is not well quantified. We measured the effect of high-severity fires in dry eucalypt forest in the Tasmanian Midlands, the driest region on the island. We compared stand structures and fuel loads in long-unburnt (>15 years since fire) and recently burnt (<5 years since fire) sites that had been completely defoliated. Even in unburnt plots, 37% of eucalypt stems and 56% of acacia stems =5 cm in diameter were dead, possibly because of antecedent drought. The density of live eucalypt stems was 37% lower overall in burnt than in unburnt plots, compared with 78% lower for acacias. Whole-plant mortality caused by fire was estimated at 25% for eucalypt trees and 33% for acacias. Fire stimulated establishment of both eucalypt and acacia seedlings, although some seedlings and saplings were present in long-unburnt plots. The present study confirmed that eucalypts in dry forests are more tolerant of fire than the obligate seeder eucalypts in wet forests. However, there were few live mature stems remaining in some burnt plots, suggesting that dry eucalypt forests could be vulnerable to increasingly frequent, severe fires.
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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".