THE DEVELOPMENT OF A REAL-TIME FOREST FIRE MONITORING AND MANAGEMENT SYSTEM
Bibliographic record
Abstract
Understanding wildfire behavior is critical for maintaining the safety of fire-fighting crews. Understanding wildfires can also result in significant fiscal savings through improved planning and resource allocating. Currently, wildfire monitoring techniques are not accurate or efficient enough to optimally monitor this natural disaster. To help remedy this, the mobile multi-sensor research group of the University of Calgary is designing an innovative, real-time, internet based wildfire monitoring and modeling system. This system has the potential to impact the process of predicting wildfire propagation, resulting in reduced damage to the environment, enhanced safety and appreciable financial savings. This paper will discuss the design and functionality of the system which integrates four components into a data acquisition/processing system and accompanying GIS based web browser. The four components are: a thermal infrared imaging sensor, wireless communications, an inertial navigation system and a wildfire prediction model based on algorithms used by Prometheus (designed by the Alberta Government in 2003). The final system is able to locate hotspots of fires within five meters, predict where a fire will most likely propagate with time, detect smoldering fires underneath surface vegetation, and detect fires through smoke, haze and darkness with the highest possible accuracy and efficiency of any commercial wildfire model available today.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".