On the Building Information Modeling of Capital Construction Projects Market Development
Bibliographic record
Abstract
Sustainable economic development of the construction industry in Russia is only possible with the application of modern BIM-technologies. The construction enterprises are facing a number of problems during the process of implementing new information technologies the main problems being: the lack of funding for business development under the conditions of protracted financial crisis within 2014-2016 time frame, as well as the lack of a national industry standard of working with BIM-technologies. The step-by-step introduction of BIM- technology in Russia is planned by the Government for the period up to 2018. We offer an economic mechanism of lower production and transaction costs of development as a result of the BIM-technology introduction, taking into account successful experience in the regional economy of the Republic of Tatarstan. In our opinion the practically well-proven mechanism of introduction of innovative technologies in regional economy in public construction companies, constructing infrastructure projects, and private construction firms and approbation in practice and summarizing the lessons learned will enable one to develop building information modeling services market and ensure the sustainable economic development of the construction industry in the region. The improvement of the efficiency and transparency of the building production will create conditions so that they attract domestic and foreign institutional investors which, in turn, will allow a business to implement strategic development program for the prosperity of the economy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".