Assessing telecommunication vulnerabilities to wildfire in Alberta, Canada
Bibliographic record
Abstract
Wildfires can pose profound threats to public safety. During escalated wildfire situations, communication among responding agencies is critically important, as is communication with the affected public. Telecommunication vulnerabilities to wildfire have been largely ignored in fire risk assessment and human dimensions research. This study examines telecommunications vulnerabilities to wildfire in Alberta, Canada, consisting of three objectives: (1) determine the extent of cell phone coverage in Alberta in relation to service demand areas (SDA's); (2) identify to what extent telecommunications infrastructure and SDA's are exposed to potential wildfire; and (3) identify SDA's that have high wildfire exposure and potentially limited cellphone coverage. We used a landscape fire exposure metric and communications viewshed to identify wildfire-exposed areas lacking cellular service within the Canadian province of Alberta. A directional vulnerability analysis highlighted the telecommunications infrastructure most at risk. Service demand areas (SDAs) prone to cellular coverage gaps (i.e., road and trail networks) were used to assess public safety implications. A patchwork of cellular coverage blankets the province, which is heavily influenced by population distribution and hindered by terrain. Communications towers frequently occur in locations with high wildfire exposure (n = 186) and individual towers are vulnerable to hundreds of potential fire pathways. Expansive areas along roadways and trail systems lack cell phone coverage in addition to being exposed to potential wildfire, posing a threat to public safety. These findings may inform future wildfire preparedness and mitigation plans, fuel reduction treatments, and telecommunications infrastructure improvements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".