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Record W4383620737 · doi:10.55908/sdgs.v11i2.289

Alexandrovich, K. A. (2023). Taking into Account the Impact of Various Types of Losses When Using Information Modeling Technology in Construction

2023· article· en· W4383620737 on OpenAlexaff
Борис Хрусталев, Grabovy Pyotr Grigoryevich, Grabovy Kirill Petrovich, Kargin Alexey Alexandrovich

Bibliographic record

VenueJournal of Law and Sustainable Development · 2023
Typearticle
Languageen
FieldEngineering
TopicConstruction Management and Sustainability
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsReal estateComputer scienceRisk analysis (engineering)Control (management)Work (physics)Process managementBusinessEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Objective: The research is devoted to the application and formation of new scientific approaches, the development of practical recommendations, the use of information modeling technology in the activities of the construction complex enterprises at the stages of the life cycle of real estate objects to increase competitiveness and reduce the cost of final products, as well as reduce the timing of projects. Method: Such research methods as theoretical analysis and empirical study, including the main scientific approaches were used: "dialectical," "systemic," "dynamic," "variant," "balance," "modeling." An analysis of the activities of a number of enterprises in the construction industry of the Penza region was carried out. A quantitative assessment of external and internal environment factors affecting the activities of enterprises and their classification was carried out taking into account the influence of each element, and an algorithm of control models was developed using information models in risk conditions, taking into account losses of different levels. Results: During the study, it was established that the formation of losses occurs in conditions of constantly changing factors of the external and internal environment, which require their assessment when determining the main parameters and efficiency of the activities of enterprises of various organizational and economic redistributions of the complex. When moving from stage to stage, loss accumulation and transformation occurs. Conclusions: A flexible management model is proposed that allows you to respond to all changes in the external environment, as well as timely manage costs in the internal environment, including the timing of work, which will allow you to quickly adjust the progress of production processes in space and time when any types of risks occur in the activities of the construction complex enterprises.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0340.020

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes1
Has abstractyes

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