The sum of small parts: changing landscape fire regimes across multiple small landholdings in north-western Australia with collaborative fire management
Bibliographic record
Abstract
Fire is a natural process in tropical savannas, but contemporary cycles of recurrent, extensive, severe fires threaten biodiversity and other values. In northern Australia, prescribed burning to reduce wildfire incidence is incentivised through a regulated emissions abatement program. However, only certain vegetation types are eligible; also, managers of small land parcels are disadvantaged by the program’s transaction costs and interannual variability in management outcomes. Both impediments apply to landholders of the Dampier Peninsula, north-west Australia. Nevertheless, Indigenous rangers, pastoralists and other stakeholders have collaborated for 5 years to manage fire across their small holdings (300–2060 km2). We used remote sensing imagery to examine the project’s performance against seven fire regime targets related to biodiversity, cultural and pastoral values. At the scale both of individual landholders and the entire Peninsula (18 500 km2), the project significantly reduced the extent of annual fire, high-severity fire, mid-late dry season fire, fire frequency and severe fire frequency. The project significantly increased the graininess of burnt and unburnt areas and the extent unburnt for 3+ years more than tripled. The project demonstrates that cross-tenure collaboration can overcome the challenges of managing fire on small land parcels. However, this project’s sustainability depends on securing ongoing funding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".