Mapping the wildfire threat to boreal communities
Bibliographic record
Abstract
Considerable interest and effort in identifying significant wildfire risk is drawn from the catastrophic impact of increasingly large and destructive wildfires on people, their health and safety, and the values and developments that support them. Improved methods include updated efforts to represent hazard and exposure across landscapes and within communities. The tools and techniques applied and evaluated here are collectively called Wildfire Exposure Assessment, a process developed and published by Jennifer L. Beverly (University of Alberta) and others. The simplicity and speed of the Exposure Assessment method make it an important prospect for communities planning for the protection of their citizenry and the values that support them. It makes few assumptions about factors difficult to assert and quantify over planning time horizons. Applied here specifically for communities in the Boreal biome, its utility is evaluated for three communities: Anchorage and Fairbanks in Alaska, and Whitehorse in the Yukon Territory. Further, it has been applied to all lands for both Alaska and the Yukon Territory based on vegetation classification from 2014. To this day, all spatial depictions of wildfire hazard begin as vegetation maps. The NASA Arctic Boreal Vulnerability Experiment (ABoVE), among its many environmental assessments, produced a consistent, historical catalog of vegetation and land cover classifications over the life of the LANDSAT period of record, dating to 1984. These provided a consistent and useful set of products for use in establishing the spatial distribution of wildfire hazards and the utility these datasets could provide for the three boreal communities considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".