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Record W4224124273 · doi:10.1061/9780784484180.019

Innovative CLT Gravity and Lateral Systems for Vancouver School Projects

2022· article· en· W4224124273 on OpenAlexaffabout
Md Shahnewaz, Carla Dickof, Nick Bevilacqua, Thomas Tannert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsPositive Living NorthSurgical Specialties (Canada)Quest University CanadaUniversity of Northern British Columbia
Fundersnot available
KeywordsGeologyComputer science

Abstract

fetched live from OpenAlex

This paper presents innovative uses of cross-laminated timber (CLT) as part of the gravity and lateral force resisting systems for two school projects in Vancouver, the Sir Matthew Begbie and Bayview Elementary Schools. Natural Resources Canada Green Construction through Wood (GCWood) program supported the project construction and coordination costs associated with innovative mass timber construction. The two-storey school buildings will provide classrooms, teaching areas, gymnasium, as well as a learning center with exposed CLT walls, floors, and roof throughout; CLT walls are used for both gravity and lateral resistance in the building. At the upper storeys, hybrid steel-CLT storey-depth beams avoid intrusive drop beams below the floor. Long windows are framed with CLT panels acting as lintels. Both projects include long-span floor systems for which both deflection and vibration requirements fell outside the feasible range for CLT panels commercially available in North America; therefore, a ribbed CLT-glulam composite floor is utilized. In this paper, the innovative use of CLT includes long span composite CLT-glulam floor and roof systems, balloon-framed shear walls, and the use of concentrically and eccentrically loaded CLT lintels are discussed, focusing on the Sir Matthew Begbie Elementary School.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.377
Teacher spread0.321 · 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 designNot applicable
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

Citations1
Published2022
Admission routes2
Has abstractyes

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