NBIC-ACS Stage 2 Canadian Forest Fire Weather Index - baseline scenario, 20% and 10% annual exceedance probabilities
Bibliographic record
Abstract
The Canadian Forest Fire Weather Index (FWI) is a fire weather potential index that describes how current weather conditions and recent precipitation patterns could support a landscape fire. FWI calculations are based on Van Wagner & Pickett (1985) and Van Wagner (1987), and dependent on air temperature, relative humidity, wind speed and precipitation. The metric was developed with the worst conditions believed possible in Canada corresponding to a value of 200, although in Australia values above 200 are regularly reported. This metric was also originally developed for boreal forests, although was later generalised to different climate and vegetations. Here we provide predicted upper-bound FWI values across the Australian landscape, defined for a set of Annual Exceedance Probabilities. We calculate FWI based on the latest Bureau of Meteorology historical weather reanalysis BARRA-R2, reporting modelled hourly weather conditions from 1979 to current at a spatial resolution of approximately 11 kilometres. More than 400,000 data points at every location are then processed using the National Bushfire Intelligence Capability (NBIC) Extreme Values Analysis to predict extreme daily maximums and their likelihood. FWI is not used routinely in Australia, however it is the reference fire weather metric for other national and international contexts. As such it can play an important role in reporting the evolution of Australian bushfire hazard in international forums. All these characteristics result in datasets that are a significant advancement in defining extreme fire weather, surpassing previous approaches and offering a robust foundation for informed decision-making in managing and mitigating Australia’s growing bushfire risks in a changing climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".