Influence of fuel data assumptions on wildfire exposure assessment of the built environment
Bibliographic record
Abstract
Background Land cover information is routinely used to represent fuel conditions in wildfire hazard, risk and exposure assessments. Readily available land cover data options that vary in resolution, extent, cost and purpose of collection have become increasingly accessible in recent years. Aim This study investigates the sensitivity of community-scale wildfire exposure assessments to different land cover information products used to identify hazardous fuel. Methods Ten versions of a community wildfire exposure assessment were conducted for each of five case study locations in Alberta, Canada, by varying the input land cover data. Proportional and spatial distribution of hazardous fuels and classified exposure are compared across datasets and communities. Key results We found proportional and spatial variation of exposure values between datasets within each community, but the nature of this variation differed between communities. Land cover classification definitions and scale were important factors that led to inconsistencies in assessment results. Conclusions Readily available land cover information products may not be suitable for exposure assessments at a localised scale without consideration of unique context and local knowledge of the assessment area. Implications Results may inform fuel data selection considerations for improved results in various wildfire applications at localised scales.
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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 | 0.035 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".