Future Options for Fire Behaviour Modelling and Fire Danger Rating in New Zealand
Bibliographic record
Abstract
Bushfire research in New Zealand is focussed on developing a national fire danger rating system and fire behaviour prediction models. The approach has been to adapt the Canadian Forest Fire Danger Rating System to the New Zealand fire environment through empirical data collection from experimental fires and wildfires. Research has contributed to improved fire management and increased community and firefighter safety, but there are still significant gaps in the knowledge of fire behaviour in New Zealand fuels. Current research is focussed on developing fire behaviour models for fuel types not included in the Canadian system. Development of shrub fire behaviour models is a priority, given the significant proportion of bush fires in these fuels. However, this has highlighted the need to re-examine some of the fundamental principles guiding the New Zealand approach to fire behaviour modelling and fire danger rating. In New Zealand, fire behaviour prediction and fire danger rating are closely linked, compared to other countries where the two systems are separated. This can create difficulties in distinguishing appropriate spatial and temporal fire danger levels versus site-specific fire behaviour predictions. Other issues include selecting equation parameters and application of empirical systems in fuels different from those where observations were made. This paper reviews these issues and presents alternatives and options for the future.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".