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Record W2955753883 · doi:10.29173/mocs76

Canadian Mass Timber Demonstration Projects Initiatives

2019· article· en· W2955753883 on OpenAlexaffvenueabout
Mohammad Mohammad, Julie Tourrilhes, Richard Coxford, Miranda Williamson

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGreen buildingArchitectural engineeringEngineeringGovernment (linguistics)Order (exchange)Civil engineeringBusinessConstruction engineeringEngineering managementFinance

Abstract

fetched live from OpenAlex

In order to expedite market acceptance and facilitate the commercial uptake of wood products and systems in Canada, it is necessary to showcase such applications through high-rise and non-residential building demonstration projects. This paper presents recent initiatives by the Government of Canada focused on increasing use of wood as a green building material in infrastructure projects by supporting such demonstration projects. The objective of Green Construction through Wood (GCWood) program (launched in 2017) is to support the design and construction of several high-rise and non-residential timber demonstration buildings and bridges in Canada through expression of interest (EOI) calls. The program is also supporting research and development activities to facilitate acceptance of provisions that would allow for the construction of tall wood buildings in Canadian building codes and advanced wood education at engineering and architectural colleges and universities to help develop the future design capacity in Canada.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.810

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 communication0.0000.001
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 head0.006
GPT teacher head0.167
Teacher spread0.161 · 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

Citations4
Published2019
Admission routes3
Has abstractyes

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