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Record W1992421768 · doi:10.1139/l02-043

Contractor integrated technical information service in construction

2002· article· en· W1992421768 on OpenAlexvenueno aff
Kwang-Byung Kim, Kyung-Ho Chin, Seunghee Han, Seon-Mi Woo, Moon-young Cho

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsConstruction managementBuilding information modelingProcess (computing)Service (business)Key (lock)Integrated project deliveryEngineering managementConstruction industryPre-construction servicesProcess managementInformation sharingComputer scienceEngineeringProject managementConstruction engineeringSystems engineeringOperations managementProject planningComputer securityBusinessWorld Wide Web

Abstract

fetched live from OpenAlex

The research described in this paper implements a U.S. Department of Defense protocol entitled contractor integrated technical information service (CITIS) in a construction industry application to provide an electronic communication environment for sharing construction information among project participants. CITIS is a contractor-developed service that provides electronic access to and (or) delivery of contractual data to users. For the implementation of the CITIS concept in the construction industry, this research performs process and data modeling on a road construction project, then implements and tests a prototype. This article also introduces an overall procedure to implement the concept of CITIS in construction and identifies some challenges such as the limitations in performing process modeling in the public sector, specifically public road construction projects, the lessons learned, and suggestions in overcoming these difficulties.Key words: construction management, continuous acquisition and life cycle support, contractor integrated technical information service.

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: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.155
Teacher spread0.150 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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