System analysis of weather fire danger in predicting large fires in Siberian forests
Bibliographic record
Abstract
The prediction results of large-scale forest fire development are given for Siberia. To evaluate the fire risks, the Canadian Forest Fire Weather Index System (CFFWIS) and the Russian moisture indices (MI1 and MI2) were compared on the basis of the data of a network of meteorological stations as input weather parameters. Parameters of active fires were detected daily from the NOAA satellite data for the period of 1996–2008. To determine the length of the fire danger season, the snow cover fractions from Terra/MODIS data (2001–2008) were used. The features of fire development on territories with different types of flammable fuel are considered. The statistical analysis of the areas and number of fires typical of each vegetation class is made with the use of the GLC2000 vegetation map. A positive correlation (∼0.45, p < 0.05) between the cumulative area of local fires and the MI1 and Canadian BUI and DMC indices is revealed. The Canadian ISI and FWI indices describe best the diurnal dynamics of fire areas. The above correlations are higher (∼0.62, p < 0.05) when we select the fires larger than 2000–10000 ha in size for the forested areas. Other cases point to the lack of a linear relation between the fire area and the values of all indices, because the fire spread depends on many natural and anthropogenic factors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".