Development of Approaches and Organizational Models for the Mass Implementation of Information Modeling Technologies in the Investment and Construction Sphere
Bibliographic record
Abstract
The rapidly increasing use of building information modeling (BIM) technologies in the world is highly relevant to the search for new approaches and managerial models for enterprises in the construction sphere. As shown in the study of several developing countries, there is a certain lag in this area compared with highly industrialized countries. A comparative analysis of countries in terms of the level of spread of BIM technologies was made using open data from job search Internet sites. In this regard, the urgency of the research is due to the need to develop appropriate approaches to intensify the implementation of BIM technologies in the construction and operation of buildings. The purpose of the study is the development of methodological foundations and applied models of functional interaction between participants of construction projects based on BIM. As a working hypothesis, the authors assume that the mass application of BIM technologies is possible in providing a set of measures of different nature: market, non-market, legal, economic, and organizational. The main results of the study provided a solution to the problem of a significant expansion of the scope of BIM technologies in the construction sector through the formation of an information eco-environment for interaction of participants in the project management system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".