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Record W6959523045 · doi:10.7939/r3gt5fr9z

Mapping Canadian Wildland Fire Interface Areas

2016· dissertation· en· W6959523045 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2016
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPeanut Plant Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWildland–urban interfaceInterface (matter)Fire protectionFirefightingGeographic information systemDistribution (mathematics)

Abstract

fetched live from OpenAlex

Although wildland fires are a beneficial ecosystem process, they can also cause destruction to human-built structures and infrastructure, as evidenced by disasters such as the Fort McMurray fire in 2016 and the Slave Lake fires in 2011. This type of destruction occurs in the “wildland-urban interface” (WUI), which are areas where homes or other burnable community structures meet with or are interspersed within wildland fuels. In order to mitigate destructive WUI fires, basic information such as the location of these areas is required. Unfortunately, Canada does not have a national scale, high-resolution WUI map for use in research or fire management, which hinders our ability to study fires in WUI areas. Therefore, this study focused on defining and mapping the WUI for the national area of Canada, and analysed their spatial distribution and relationships with fuels, structures, and fires. Furthermore, two additional national maps were produced and analysed: a “wildland-industrial interface” (WII) map and an “infrastructure interface” map. These additional maps focus on the interface of wildland fuels with industrial structures (e.g. oil and gas or mining structures) for the WII, or with infrastructure values (e.g. transmission lines, railways, or roads) for the infrastructure interface. This study presents the first maps of these two interface types for anywhere in the world. Industrial structures and infrastructure are not traditionally defined as part of the WUI, but may require protection from fires and are important emerging issues. All three interface types (WUI, WII, and infrastructure interface) were defined as areas of wildland fuels which are within a variable-width buffer (maximum distance: 2400 m) from potentially vulnerable structures or infrastructure. Nationally, it was found that Canada has 32.3 million ha of WUI (3.8% of total national land area), 10.5 million ha of WII (1.2%), and 109.8 million ha of infrastructure interface (13.0%). Interface areas are typically most dense in the southern portion of the country (with the exception of the prairies and southern Ontario). Provinces with the largest amounts of interface include: Quebec, Ontario, Alberta, and British Columbia. However, the eastern provinces of Nova Scotia, New Brunswick, and Prince Edward Island have the highest densities of interface (interface as % of land area). Interface areas were also found to have higher than average hazardous fuel cover types, but lower than average area burned by wildfire. The results of this study, and in particular the interface maps, provide a baseline for future research, including fire risk mapping, change detection, and future predictions of interface areas. The maps produced in this study also have a wide variety of practical applications, including various topics in wildfire mitigation (e.g. FireSmart and industrial fire regulations), long-term planning (e.g. city planning and insurance), and wildfire decision support (e.g. fire prioritization and risk modelling).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.008
Science and technology studies0.0030.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.179
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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