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Record W2062268729 · doi:10.1139/l04-061

Collaborative work model under distributed construction environments

2005· article· en· W2062268729 on OpenAlexvenueno aff
K J Kim, C K Lee, Jaesang Kim, Eung-Sun Shin, M Y Cho

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceBuilding information modelingDatabaseInformation systemScheduleInformation modelSoftware engineeringSystems engineeringScheduling (production processes)Engineering

Abstract

fetched live from OpenAlex

Efficient management of construction project information is essential for the successful performance of a construction project. This paper investigates a standard information classification system, which represents a data model relating 3D CAD drawing, costing, scheduling, resource information, specification, and other information for multiple uses in the project. In sharing information among independent applications in distributed construction environments, a distributed transaction service module that manages information-sharing processes through an integrated database is also desirable. To examine a distributed database structure and data transaction services, this paper analyzes a standard information classification system and business process. A distributed database structure model and a data transaction service module for information sharing are designed. A prototype system is implemented and then applied to a real project for its validation. The target application area of the prototype is a high-rise steel-structure apartment building. The scope of the application includes information on the architectural and structural design, the cost, the schedule, the resources, the quality of the management, and the specifications.Key words: CIC, PMIS, 4D CAD, construction management, construction information, information classification system, integrated management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.161
Teacher spread0.157 · 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 teacher head, 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

Citations6
Published2005
Admission routes1
Has abstractyes

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