Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China
Bibliographic record
Abstract
Forest city (FC) usually refers to an urban area with high forest coverage. It is a green model of urban development that has been strongly advocated for by governments of many nations. Forest fire is a prominent threat in FC development, but the causes of fires in FCs are usually different and more complex than in pure forested areas since more socio-economic factors and human activity are involved in the ignition and spread of fire. The large and increasing number of lives being exposed to wildfire hazard highlights the need to understand the characteristics of these fires so that forest fire prediction and prevention can be efficient. In this study, Ripley's K(d) function and Random Forests (RF) were applied to analyze the drivers, spatial distribution and risk patterns of fires in Yichun, a typical FC in China. The results revealed a clustered distribution of forest fire ignitions in Yichun, as well as identified the driving factors and their dynamic influence on fire occurrence. Fire risk zones were identified based on RF modelling. Improved preventive measures can be implemented in the fire prone areas to reduce the risk of fire in Yichun by considering the factors identified in this study.
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 | 0.000 |
| 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".