Index sensitivity analysis applied to the Canadian Forest Fire Weather Index and the McArthur Forest Fire Danger Index
Bibliographic record
Abstract
A number of different methodologies are developed for examining the sensitivities of an index. These methodologies are applied to examine the characteristics of the Canadian Fire Weather Index (FWI) and the McArthur Forest Fire Danger Index (FFDI) using 8 years of gridded data throughout Australia. Percentile changes in input conditions show that the indices are similar to each other in that they are both most sensitive to wind speed, then secondly to relative humidity and thirdly to temperature. On a finer scale, a combination of the relationship between the indices and their partial derivatives shows that the FFDI is relatively less sensitive to wind speed and rainfall, and more sensitive to temperature and relative humidity, than the FWI. A method based on equilibrium values of the indices shows that the FFDI has a temperature threshold set by recent rainfall above which its sensitivity increases, resulting in some non-linearity in its relationship with the FWI. The sensitivity differences between the indices mean that the indices are complementary in that they each respond to a somewhat different set of conditions, as is shown by examining a number of recent fire events. The fire events also reveal that index values associated with dangerous fire behaviour can vary greatly between different regions. Methods to reduce the consequences of this variation are examined, including the use of index percentiles. Copyright © 2009 Royal Meteorological Society
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".