Fuel moisture moderates wildfire resistance in rainforests of south-east Australia
Bibliographic record
Abstract
Abstract In fire-prone forests of south-east Australia, rainforests have longer fire-return-intervals than the dominant and adjoining eucalypt forests, because rainforests occur in topographic positions which are typically too wet to burn. Thus, rainforests often act as natural barriers to fire spread. Although rare, severe drought can make rainforests available to burn, and this can promote very large and intense wildfires by increasing fuel availability across landscapes. Here, we explore how ten fuel moisture indices impact wildfire occurrence in rainforest patches of south-east Australia, when compared with wet and dry sclerophyll eucalypt forest types which are drier and have shorter fire-return-intervals. Vapour pressure deficit was the strongest and most ubiquitous moisture index predicting wildfire occurrence across all forest types, followed by soil moisture and live fuel moisture. Vapour pressure deficit thresholds facilitating a wildfire probability >0.5 also did not differ between forest types. However, the percentage of days exceeding vapour pressure deficit thresholds increased from rainforests to wet eucalypt forests and peaked in dry eucalypt forests. Collectively, our results suggest that the same fuel moisture thresholds promote wildfire in rainforests and fire-prone eucalypt forests; however, wildfire is less common in rainforests because they experience less time in a dry combustible state. Our results provide a framework to forecast wildfire probability across wet and dry forests at large spatial scales.
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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 | 0.001 |
| 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".