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Record W4380450830 · doi:10.52202/069179-0571

CASE STUDY ON A LARGE-SCALE TIMBER ACADEMIC BUILDING DESIGNED TO ADDRESS CURRENT INDUSTRY CHALLENGES

2023· article· en· W4380450830 on OpenAlexaff
Javier Fierro, Herry Chen, Tyler Hull, Andrea Atkins, Daniel Lacroix

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban and spatial planning
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsArchitectural engineeringFlexibility (engineering)AmenitySustainabilityStructural systemCivil engineeringEngineeringConstruction engineeringComputer scienceBusiness

Abstract

fetched live from OpenAlex

The need for more sustainable construction has led to the desire to increase the use of wood materials in our buildings.However, challenges in terms of larger column grids, future design flexibility, and fire resistance have occurred in institutional, commercial, and industrial building types that have hindered mass timber's adoption.This study reviews these challenges and provides insight into the design of a mass timber academic building as a viable design solution.The building creates open-concept and future flexible spaces by taking advantage of several novel structural solutions, while achieving fire resistance requirements.The structural solutions include the long span hollowcore mass timber floor panels for large amenity areas, two flat plate "service towers", and a combination of timber braced frames and CLT shear walls for the lateral system, in order to meet the challenges faced.

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)0.001
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.106
GPT teacher head0.345
Teacher spread0.239 · 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 designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

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