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Record W2263281101

Business Model for BIM-based Services

2013· article· en· W2263281101 on OpenAlexaff
Islam Mazen, Tamer E. El-Diraby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBuilding information modelingBusinessProcess managementProcess (computing)Business processBusiness modelIndustrialization of services business modelKnowledge managementMarketingBusiness process modelingEngineeringOperations managementComputer scienceWork in process
DOInot available

Abstract

fetched live from OpenAlex

Building information modeling (BIM) has emerged as a new construction technology that has a positive impact on improving the construction industry. The adoption of BIM in the construction industry is still slow within the construction firms. Some companies have successfully implemented BIM; however, they believe they can get more value if the process is well coordinated among the stakeholders. Other companies fail to start the process because of the high amount of required investments, training and organizational barriers. This gap between the need for BIM services and the ability of companies to implement them in their business processes created a business opportunity for selling BIM services to the clients. This paper proposes a business model for selling BIM design services to different customer segments that requires assistance in BIM implementation. The business model acts as a roadmap for companies to sell BIM design services.

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)0.003
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0100.008
Open science0.0030.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0170.011

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 head0.183
Teacher spread0.175 · 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
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

Citations1
Published2013
Admission routes1
Has abstractyes

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