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Record W4402030817 · doi:10.1002/9781119902317.ch13

The Language of Digital Technologies

2024· other· en· W4402030817 on OpenAlexaff
Leonardo Manzione, Sílvio Burratino Melhado

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceComputer graphics (images)LinguisticsPhilosophy

Abstract

fetched live from OpenAlex

In reviewing the vocabulary of digital construction, this chapter clarifies the concepts of language and culture involved in the design and management activities around the conception of BIM Execution Plans, the use of Common Data Environment platforms, as well as in all the collaborative production of information according to the terminology established by BIM standards. There is an issue that new digital technologies are creating a diverging vocabulary that makes a common understanding more difficult. For example, the specification of information management for the delivery phase of construction projects using building information modelling and the recently published ISO 19650 series on information management are portrayed as essential guides to the architectural design and management in level 2 of BIM. They introduce a considerable amount of new concepts and definitions, which are discussed and interpreted in this chapter, with regard to the different experiences of countries’ adaptation to digital construction demands.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0040.022
Scholarly communication0.0120.015
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.003

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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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