Spatio-Temporal Forest Fire Risk Assessment in Huichuan District, China: Integrating Fuel Load, Topography, and Human Activity
Bibliographic record
Abstract
This study aimed to characterize spatial risks of forest fires in Huichuan District, Zunyi City, Guizhou Province, China. The National Forest and Grassland Fire Risk Census Secondary Platform provided 16 indicators of forest fire risk. This study assessed forest fire risk based on field fuel surveys, multi-source data sharing, mathematical statistics, and literature analysis. The following results were obtained: (1) Of the 21,210 small classes of forest fire identified for Huichuan District, 9,263 (43.67%), 11,941 (56.3%), and 6 (0.02%) were high, medium-high, and medium low risk, respectively. (2) Of the 1,988 standardized grids: 68, 1,523, 208, 186, and 3 showed high, medium-high, medium-low, low, and no risks, respectively; 64.18% showed medium-high to high risks. (3) Township-level risk assessment divided towns into three risk categories: low, medium-low, and medium-high, to which were assigned three, two, and nine township-level assessment units, representing 0.91%, 5.49%, and 93.67% of the total area, respectively. Among the township evaluation units, two, nine, and three showed medium-low, medium-high risk, and low risks, respectively. Gaoqiao Street, Donggongsi Street, and Dalian Road have medium-low risk grades; the other nine township evaluation units have medium-high risk categories. Ximalu Street, Shanghai Road, and Gaoqiao Street have low-risk grades.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".