Study on Forest Fire Risk Analysis Based on Drought Scenarios
Bibliographic record
Abstract
The number of forest fires is increasing worldwide owing to climate change. Forest fires linked to drought are becoming more frequent and severe than ever before. This causes massive damage. However, instead of quantification, studies have been conducted on the relationship between drought and forest fires. Therefore, this study quantitatively analyzed the increase in forest fire risk based on a drought scenario. We defined a 30-year frequency drought scenario for the Gyeongsangbuk-do Province, where the most forest fire damage occurred in 2022, and analyzed the risk of forest fires by calculating the Fire Weather Index (FWI) used in the Canadian Forest Fire Danger Rating System (CFFDRS). The precipitation in the drought scenario was 581 mm/year that was equivalent to 52% of the average annual precipitation. We noted that the drought Fire Weather Index (DFWI) was approximately 1.6 times greater than the average Fire Weather Index (AFWI). We quantitatively confirmed the extent to which the risk of forest fire increased under the influence of drought. Therefore, measures should be established not only for drought but also for forest fires when a drought occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".