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Record W2338358826 · doi:10.14288/1.0076329

Risk of wildfires with known ignition points : case of residential buildings

2015· article· en· W2338358826 on OpenAlexaboutno aff
Elmira Kalhor, Vanessa Valentin

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceForensic engineeringArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

Wildfire is considered the dominant disaster in many regions of the world including the United States, Australia, Canada and parts of Europe. However, unlike other natural disasters, such as flooding, earthquakes and hurricanes, the risk of wildfire to the built environment is not vigorously studied. Most of the research in the wildfire risk management area is limited to the study and management of wildfire within the wildland. On the other hand, there is an increasing progress of housing projects towards the natural lands. The Wildland Urban Interface (WUI) is where the developed and undeveloped areas meet. Because of high vulnerability of the WUIs to wildfires, there is a need to identify, quantify and manage the expected damage of wildfires to the WUIs. This study calculates the risk of wildfires to residential buildings considering a specific ignition point. The model inputs include the spatial distribution of the buildings, an absolute or proxy value for the damage from wildfire, and atmospheric and landscape attributes needed to simulate the fire propagation on a specific land. The model outputs are the distribution of damage at each time interval from the initial ignition and total risk of a fire with a given ignition point.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.777

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)0.0000.000

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 head0.007
GPT teacher head0.177
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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

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