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Record W2936647771 · doi:10.29173/mocs64

Construction industrialization and it integration: how tall wood buildings can show the right path towards construction 4.0

2017· article· en· W2936647771 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsBuilding information modelingSupply chainArchitectural engineeringConstruction industryIndustrialisationPath (computing)Order (exchange)Construction engineeringComputer scienceEngineeringBusinessOperations managementMarketingEconomics

Abstract

fetched live from OpenAlex

The construction industry has been considered as refractory to information technologies. However, new technological approaches, such as the Building Information Modeling (BIM), are becoming increasingly adopted and are promising better collaboration, better flow optimization and greater integration of the supply chain. These changes are preparing the industry for the advent of "Construction 4.0", a more federating concept than BIM. This article explores the concept and shows how tall wood buildings could show the right path and lead the way for the rest of the industry. The research shows that while tall wood construction actually presents the potential to fit the construction 4.0 requirements, current practices are far from being uniform from one project to another. It also illustrates the need for a close collaboration between practitioners and researchers in order to overcome the current challenges.

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.203
Teacher spread0.190 · 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