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Record W3176203241 · doi:10.1051/e3sconf/202127503027

Project cost Management Strategy based on Big Data and BIM

2021· article· en· W3176203241 on OpenAlexaboutno aff
Ping Wu

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)ChinaProcess (computing)BusinessOperations managementEnvironmental economicsOperations researchIndustrial organizationEconomicsComputer scienceEngineeringGeography

Abstract

fetched live from OpenAlex

With the development of socialist market economy, the current project cost management system in China has gradually exposed some disadvantages, which affect the normal operation and development of construction enterprises to a certain extent. This paper analyzes the research status of PCM, discusses the characteristics of BD technology, the concept of BIMT and the concept of PCM, and finds the method of mutual information feature selection. The results show that in 2020, the output value of construction projects in China is the largest, with 5.54 billion yuan in the first quarter, 5.83 billion yuan in the second quarter, and 6.77 billion yuan in the third quarter. Due to the large number of participants, long construction period and large amount of information in the construction process, these greatly increase the difficulty of PM, but also increase the difficulty of PCM, making some cost problems easier to appear.

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)0.004
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.224
GPT teacher head0.331
Teacher spread0.107 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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