The influence of ignition technique on fire behaviour in spinifex open woodland in semiarid northern Australia
Bibliographic record
Abstract
Unplanned, unmanaged wildfires are a significant threat to people, infrastructure and ecosystems around the world. Managed, planned burning is widely used for reducing the incidence, extent or intensity of wildfires. Fire weather and the season of burning are recognised as crucial factors influencing fire behaviour but the demonstrated influence of ignition technique on fire behaviour is not as prominently discussed in relation to planned fires. We found wildfires, irrespective of season, burnt the ground layer more completely (i.e. were less patchy) and produced greater crown scorch severity than did planned fires in a spinifex (Triodia spp.)-dominated open woodland. Fires ignited with a 50-m line burning with the wind produced significantly higher intensities than did line ignition against the wind, and spot ignitions with or against the wind. These data suggest that the higher severity of wildfires in spinifex-dominated habitats is strongly influenced by long fire fronts, in addition to fire season and weather conditions. This study supports the value of planned burning for reducing fire severity and highlights the value of spot ignitions in ecological burning to create a patchily burnt landscape, with limited canopy severity.
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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".