Review of wildfire indices : Indices applicable for a Swedish context
Bibliographic record
Abstract
Wildfire risk indices can be used in order to be better prepared for wildfires. In Sweden, the Canadian Fire Weather Index (FWI) is currently used to predict fire behaviour based on meteorological data such as wind velocity, temperature and rainfall. Additionally, the HBV model, used to calculate moisture content in soil layers, serves to predict the ignition risk in Sweden. There are however many different wildfire indices used in the world and this study aims to review and identify wildfire risk indices that could also be applicable for a Swedish context. Eleven wildfire risk indices or methods were discovered through a comprehensive literature review. These are described in the report, including required input parameters, information about testing or validation of the method and finally advantages and disadvantages of the method with regards to its potential use in a Swedish context. Four indices were deemed more relevant and suggested for further analysis including evaluation against wildfire data in Sweden: the Fire Weather Index (FWI), the Fosberg Fire Weather Index (FFWI), the Keetch-Byram Drought Index (KBDI) and the Nesterov Index.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".