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Record W4367554022 · doi:10.1061/9780784484777.024

Case Study: A High-Rise Mass Timber Building in Vancouver, British Columbia

2023· article· en· W4367554022 on OpenAlexaffabout
C. Dickof, R. Jackson, Jenna Kim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsSurgical Specialties (Canada)Quest University Canada
Fundersnot available
KeywordsBraceShear wallStructural engineeringEngineeringSeismic analysisDissipationSlip (aerodynamics)Cross laminated timberGeotechnical engineering

Abstract

fetched live from OpenAlex

The Keith Drive Office building, a 10-story, exposed tall wood structure located in Vancouver, Canada, will serve as landmark building in central Vancouver. The 43-m-tall structure is comprised of nine floors of mass timber gravity and lateral construction over a concrete podium and four levels of below-grade concrete parking. The 2-h fire rated exposed gravity structure consists of cross laminated timber (CLT) floor panels supported on dropped perimeter glulam beams and flush interior steel beams. The seismic force resisting system (SRFS) consists of perimeter timber braced frames and interior balloon framed CLT shearwalls. Tectonus resilient slip friction joint (RSFJ) devices specified at each brace, and both ends of each shear wall, allowing energy dissipation without damage to the structural system. To support the SFRS design, Non-linear time history analysis (NLTHA) was completed as part of a larger peer review. Iterative NLTHA was used to optimize the RSFJ design for the 2% in 50-year design earthquake. NLTHA was also implemented at 130% and 150% of the design UHS to review the collapse risk for an earthquake exceeding the design earthquake.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.216
Teacher spread0.207 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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