Fire Activity and Their Relationship with the Global Fire Weather Index Database Components in Guinea
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Bibliographic record
Abstract
The relationships between the Canadian Fire Weather Index (FWI) System components and the monthly burned area as well as the number of active fire which has taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua/TERRA were investigated in 32 Guinean stations between 2003 and 2013. A statistical analysis based on a multi-linear regression model was used to estimate the skills of FWI components on the predictability of burned area and active fire. This statistical analysis gave performances explaining between 16 to 79% of the variance for the burned areas and between 29 and 82% of the variance for the number of fires (P<0.0001) at lag 0. Respectively 16 to 79 % and 29 to 82 % of the variance of the burned areas and variance for the number of fires (P<0.0001) at lag0 can be explained based on the same statistical analysis. All the combinations used gave significant performances to predict the burned areas and active fire on the monthly timescale in all stations excepted Fria and Yomou where the predictability of the burned areas was not obvious. We obtained a significant correlation between the average over all of the stations of burned areas, active fires and FWI composites with percentage of variance between (75 to 84% and 29 to 77%) for active fires and burned areas at lag0 respectively. While for burned area peak (January), the skill of the predictability remains significant only one month in advance, for the active fires, the model remains skilful 1 to 3 months in advance. Results also showed that active fires are more related to fire behavior indices while the burned areas are related to the fine fuel moisture codes. These outcomes have implications for seasonal forecasting of active fire events and burned areas based on FWI components, as significant predictability is found from 1 to 3 months and one month before respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it