Using Remote Sensing Data and Raster Analysis Tools to Assess Fire Hazard Severity in South-Central British Columbia
Bibliographic record
Abstract
Wildfire is a significant threat to ecosystems and human safety, exacerbated by climate warming. The Penticton region of British-Columbia, Canada is an area which is experiencing increasingly worsening wildfire events. These natural disturbance events represent a significant threat to local ecosystems, property and human life and wellbeing. As fire conditions worsen, and the population density of this region increases, landscape analysis of fire hazard levels is necessary to direct emergency service management prior to and during wildfire events and to inform policy on how to manage these natural disasters. To assess fire hazard levels, a GIS-based multi-criteria analysis was performed to understand fire hazard spatially, subdivided into low, moderate, high, and severe hazard areas. Two models were built to achieve this, taking into account commonly used variables employed to assess fire hazard severity around the world. To identify potential differences in hazard assessment, the models weighted these variables differently from one another. Fire location points from the year 2000 to 2021 were overlayed with each respective model output. Model 1 spatially overlapped with 73.88% of these fires, while model 2 spatially overlapped with 74.35%. These results can help identify areas of elevated hazard under ideal burning conditions, inform deployment of emergency services and resources, and provide a framework for using a GIS to conduct a fire hazard landscape assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".