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A Systematic Review of International Standards and Codes Towards a Unified Framework for Fire Financial Losses

2025· article· en· W7090704344 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Physics Conference Series · 2025
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationFire protectionEuropean unionComparabilityCost–benefit analysisInternational standardizationFire safety

Abstract

fetched live from OpenAlex

Abstract In England, fire incidents resulted in an estimated £12 billion in losses in 2022, affecting lives, properties, businesses, and communities. However, current standards and codes for evaluating fire costs remain incomplete. The developed research aims to enhance a unified model for assessing direct and indirect fire losses, and fire protection costs based on a systematic review conducted screening 91 national and international standards from the UK, USA, Canada, Australia, New Zealand, China, Taiwan, as well as the European Union and the International Organization for Standardization with 27 relevant documents selected for analysis. Findings reveal that for property damage, standards often use formula-based approaches such as multiplying the average loss per square meter by construction cost or component-specific calculations. Detailed methodologies for indirect costs remain limited. Additionally, active and passive fire protection measures are estimated as the sum of installation and maintenance expenses. The research demonstrates the critical need for a standardised framework to accurately assess fire financial losses, enhancing decision-making for fire protection investments and mitigating the economic burden of fire incidents.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.296
Teacher spread0.283 · 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 designSystematic review
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
Published2025
Admission routes1
Has abstractyes

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