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Record W3153970484 · doi:10.1007/978-3-030-70566-4_65

Industry 4.0 and BIM: Do They Share the Same Objectives?

2021· book-chapter· en· W3153970484 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.

Bibliographic record

VenueLecture notes in mechanical engineering · 2021
Typebook-chapter
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSoftware deploymentEmerging technologiesBusinessEngineeringDigital transformationArchitectural engineeringComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract In recent years, many changes have been taking place within the construction sector which is much more prone to innovate than in the past. New forms of processes are emerging with the introduction of digital technologies. This article aims to shed light on recent scientific advances that link Industry 4.0 to this sector. To this end, a review of articles published over the past 10 years reporting experiences and gains from 4.0 technologies applied to construction was conducted. It turns out that recent technological developments have brought new functionalities and new perspectives to companies. Some of these were not initially claimed with the sole deployment of a BIM approach. These new opportunities have ultimately raised questions as to whether and how they could impact the speed at which a digital transformation of the sector could take place.

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), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.004
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 head0.008
GPT teacher head0.198
Teacher spread0.189 · 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