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Record W6929462230 · doi:10.4224/rr3q-hm83

National Energy Code of Canada for Buildings: 2020

2022· standard· en· W6929462230 on OpenAlexfundvenueaboutno aff

Bibliographic record

VenueNPARC · 2022
Typestandard
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Resources Canada
KeywordsBuilding codeEfficient energy useHVACService (business)Code (set theory)AmpacityCommission

Abstract

fetched live from OpenAlex

The National Energy Code of Canada for Buildings (NECB) 2020, developed by the Canadian Commission on Building and Fire Codes with the support of Natural Resources Canada, and published by the National Research Council of Canada, sets out technical requirements for the energy-efficient design and construction of new buildings and additions. Several technical changes have been incorporated in this new edition, improving the level of energy efficiency provided by the Code and expanding compliance options. Significant changes in the NECB 2020 • The application of the Code is extended to cover alterations, such as tenant improvements, to buildings originally constructed in accordance with the NECB. • Maximum overall thermal transmittance values for opaque building assemblies and fenestration are reduced to improve the thermal performance of the building envelope. • Whole-building airtightness testing is introduced as an option for complying with air leakage requirements. • Lighting power densities are updated to reflect improvements in the efficacy of lighting products. • Performance requirements for heating, ventilating and air-conditioning (HVAC) and service water heating equipment are updated to align them with relevant standards and regulations. • The trade-off compliance paths for HVAC and service water systems, which were complex and not widely used, are removed. • A new compliance path with 4 energy performance tiers is introduced to provide a framework for achieving higher levels of energy efficiency in buildings.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.668
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.256
Teacher spread0.243 · 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.

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

Citations6
Published2022
Admission routes3
Has abstractyes

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