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Record W4306399776 · doi:10.1002/ecs2.4255

Wildfire evacuation patterns and syndromes across Canada's forested regions

2022· article· en· W4306399776 on OpenAlexaffabout
Alan J. Tepley, Marc‐André Parisien, Xianli Wang, Jacqueline Oliver, Mike Flannigan

Bibliographic record

VenueEcosphere · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaThompson Rivers UniversityCanadian Forest Service
Fundersnot available
KeywordsVulnerability (computing)GeographyClimate changeLand coverDisturbance (geology)Land useEnvironmental resource managementEcologyEnvironmental scienceBiologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

Abstract Human exposure to wildfire is increasing in many regions globally—a trend likely to continue as climate change drives increases in wildfire activity and human populations continue to expand into fire‐prone landscapes. In Canada, this trend is reflected by a steady increase in the annual number of wildfire evacuations since the 1980s. Evacuations can be costly and cause severe stress, even when homes remain undamaged. Because many factors driving community vulnerability are likely correlated, classifying at‐risk communities into groups whose members share common drivers of wildfire vulnerability will be helpful in identifying the key wildfire evacuation “syndromes” that are repeated in different parts of the landscape. Understanding these syndromes will aid in anticipating and mitigating the effects of future fire exposure. Here, we classify the populated places across Canada's forested regions into 19 groups using variables describing their potential vulnerability to wildfire, including the surrounding land cover, land use and infrastructure, and the local fire regime. Then, we evaluate the utility of these groups by comparing actual wildfire exposure among the groups using a unique dataset of 1043 wildfire evacuations from 1980 to 2019. We identified three main evacuation syndromes that represent 79% of all evacuations and are distinct in their geographic distribution, the characteristics of the fires that drove the evacuations, the communities exposed, and the likely mode of evacuation. In remote areas dominated by conifer forest, evacuations were driven primarily by lightning‐ignited fires in the summer. Exposed communities typically lacked access to the road network, making it important to plan for evacuation by air. In less remote mixedwood forest areas, evacuations were driven largely by human‐ignited fires in the spring, and most communities had access to major roads. In interior British Columbia, evacuations were mainly in the summer and driven by both lightning‐ and human‐ignited fires. These areas experienced the greatest increase in evacuation frequency over the last two decades, reflecting the local trend of increasing wildfire activity. These differences highlight how the major risk factors vary spatially across the forested regions and temporally over the fire season—knowledge that will facilitate more effective planning for future fire seasons.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.006
GPT teacher head0.204
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2022
Admission routes2
Has abstractyes

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