Global patterns in fire leverage: the response of annual area burnt to previous fire
Bibliographic record
Abstract
Prescribed fire is practiced around the world to reduce the effect of unplanned fire, but we hypothesise that its effectiveness is proportional to the mean annual area burnt by unplanned fire, which varies among biomes. Fire history mapping was obtained for six global case studies from a range of biomes: Portugal, Spain (both Mediterranean), Alberta (boreal Canada), Sequoia and Kings Canyon National Parks (montane USA), the Sandy Desert (arid Australia) and Kruger National Park (South African savanna). Leverage is the unit reduction in unplanned fire area resulting from one unit of previous fire as measured at a regional scale over a long period. We calculated leverage for each case study using statistical modelling of annual area burnt, controlling for annual climatic variation. We combined the six leverage values with those from four previously published cases to conduct a global test of our hypothesis. Leverage was high in Portugal (~0.9) and moderate in the Sandy Desert (~0.3). However, the other case studies showed no evidence of leverage: burnt area was not influenced by past fire. In all regions, climatic variation had more influence than past area burnt on annual area burnt. The global analysis revealed a positive relationship between mean area burnt and leverage but only when outlying cases were removed. In biomes with low fire activity, prescribed fire is unlikely to reduce unplanned fire area at all, while for many others, the return for effort is likely to be low. Lessons derived from one biome cannot necessarily be applied to another.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".