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Record W6910491065 · doi:10.4224/40002009

National Fire Code of Canada: 2015

2015· standard· en· W6910491065 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2015
Typestandard
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDemolitionHazardous wasteBuilding codeFire protectionCommissionTable (database)HarmonizationLicense

Abstract

fetched live from OpenAlex

The National Fire Code of Canada 2015 (NFC), published by NRC and developed by the Canadian Commission on Building and Fire Codes, sets out the technical provisions regulating activities related to the construction, use or demolition of buildings and facilities, the condition of specific elements of buildings and facilities, and the design or construction of specific elements of facilities related to certain hazards as well as the protection measures for the current or intended use of buildings. There are 77 technical changes in the NFC. The most significant changes relate the construction of six-storey buildings using combustible construction. As a result, eight additional protection measures have been added to address fire hazards during construction when fire protection features are not yet in place. Following mid-rise combustible construction, the second most significant change in the NFC 2015 is the introduction of the classification system used by Workplace Hazardous Materials Information System (WHMIS) to define dangerous goods. These changes reflect harmonization of the dangerous goods classification system with the Globally Harmonized System (GHS) recently adopted in Canada. The NFC 2015 has been re-organized to consolidate relevant information. Each Part now contains the Prescriptive Requirements, followed by the Attribution Table and related (appendix) Notes.

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.009
Science and technology studies0.0060.002
Scholarly communication0.0070.003
Open science0.0040.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0550.037

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 head0.031
GPT teacher head0.291
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreOther

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 routes2
Has abstractyes

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