Reviewing the “Hottest” Fire Indices Worldwide
Bibliographic record
Abstract
Wildfire indices are used globally to quantify and communicate a wide range of fire characteristics, including fire danger and fire behaviour. Wildfire terminologies, definitions and variables used to compute fire indices vary broadly. This makes it difficult to compare them under a common framework for regional assessment and for future improvements under changing climate and land-use/land-cover conditions. This paper reviews 24 fire indices used worldwide and proposes a simple framework within which they can be classified based on constitutive inputs used for their computation. We differentiate between constitutive inputs that are raw or directly measurable variables such as fuel, weather and topography (referred to as Level 1 inputs) and calculated constitutive inputs such as fuel moisture (as a function of ecology and hydrometeorology); fire behaviour (as a function of spread, energy, and ignition); and dynamic meteorology. These six calculated constitutive inputs are referred to as Level 2 inputs. Based on this classification, our findings indicate that the Burning Index from the United States National Fire Danger Rating System (NFDRS) and the Fire Weather Index from the Canadian Forest Fire Danger Rating System (CFFDRS), used by many countries worldwide, utilize the most comprehensive set of Level 2 inputs. In addition, the Level 2 input that is most frequently used by all fire indices is fuel moisture as a function of hydrometeorology and the least integrated input is that of fire ignition. We further group the fire indices in three types: fire weather, fire behaviour, and fire danger indices, according to the open literature definition of their predictant outputs and examine the specific constitutive inputs used in their computation. Most fire indices are based on Level 2 inputs (which use Level 1 inputs) and are predominantly fire danger and fire behaviour indices. This is followed by fire indices that use a combination of both Level 1 and Level 2 inputs, separately and are mostly fire danger indices. Only a few fire indices are computed solely with raw Level 1 inputs and are mainly fire behaviour indices. Providing a comprehensive view of the existing wildfire indices’ utilization and computational structure is expected to be a helpful resource for wildfire researchers and operational experts worldwide. 2
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".