PREDICTION OF FOREST FIRE USING WIRELESS SENSOR NETWORK
Bibliographic record
Abstract
GAO DM, YIN XF & LIU YF. 2015. Prediction of forest fire using wireless sensor network . High incidence and destructiveness of forest fire determine the importance of forest fire prevention. Based upon local weather observations, one of the most recognised and widely applied fire-danger rating systems, the Canadian Forest Fire Weather Index (CFFWI) System, was found highly correlated with forest fire occurrence. This fire-danger system was evaluated and calibrated for Nanjing, the capital of Jiangsu Province, China. This wireless sensor network was utilised for collecting 24-hour weather data continuously. The components of CFFWI System, based upon the weather data for this region, provided insight of possibility of forest fires. The system showed that spring and autumn were seasons when the occurrences of forest fires were more dangerous. The components of the system indicated the possible dangers of the ignition of forest before the fire started. The components of CFFWI System are good indicators of fire danger in the Nanjing region of China and can be expended to build a working fire-danger rating system for the region.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".