Study on Assessment of Beijing Forest Fire Danger
Why this work is in the frame
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Bibliographic record
Abstract
Fire danger rating system has already been a significant tool for modern forest fire management. This paper uses Canadian fire weather index system to assess the forest fire danger of Beijing based on the local fire weather. Using air temperature, relative humidity precipitation and wind speed on noon, we calculate the daily component indexes of FWI with a SAS program. The definition of FWI components is given for Beijing forest fire danger rating on the basic of analysis of the indexes distribution. We make maps of fuel humidity codes and fire weather index by using interpolation method. The relationship between forest fire and relative fire danger indexes in 2004 and 2005 spring indicates that the FWI system can reflect the fire danger rating and can be used to assess and forecast the forest fire danger rating. The season severity rating shows that the fire danger in 2005 is higher than that in 2004. Finally, the influence of different interpolation on the results is discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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