Mapping the wildfire threat to boreal communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. \nThe 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. \nTo 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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it