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Record W2788546687 · doi:10.1139/cjce-2016-0552

Developing fire safety engineering as a practice in Canada

2018· article· en· W2788546687 on OpenAlexafffundvenueabout
Hailey Quiquero, Matthew Smith, John Gales

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsYork UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsFire protection engineeringFire safetySubdivisionEngineeringFire protectionEngineering ethicsEngineering managementTransport engineeringArchitectural engineeringPublic relationsCivil engineeringPolitical science

Abstract

fetched live from OpenAlex

The goal of fire safety engineering (FSE) is to design a strategy that meets human safety and property protection goals with an optimized solution. In comparison to international practice, Canada could be considered highly underdeveloped in a technical perspective of performance versus prescription. In Canada, FSE is a subdivision within civil and mechanical engineering, rather than treated as a profession in and of itself. With a requirement for more complex infrastructure to meet Canadian societal needs, there is stimulus that is fostering a demand to create professionals educated with FSE skill sets. This literary study therefore aims to present a state-of-the-art review of the design practice of FSE in Canada and abroad with explicit focus on performance-based fire design for the Canadian practitioner who seeks to develop their expertise in this subject.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0160.009
Scholarly communication0.0090.002
Open science0.0020.003
Research integrity0.0020.002
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 head0.004
GPT teacher head0.181
Teacher spread0.176 · 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 designQualitative
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

Citations6
Published2018
Admission routes4
Has abstractyes

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