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Record W2104775765

IMPLEMENTATION OF A DISTRIBUTED, MODEL-BASED INTEGRATED ASSET MANAGEMENT SYSTEM

2003· article· en· W2104775765 on OpenAlexfundvenueno aff
Mohammad A. Hassanain, Thomas Froese, Dana J. Vanier

Bibliographic record

VenueNPARC · 2003
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaPublic Works and Government Services Canada
KeywordsInteroperabilityComputer scienceSoftware engineeringSystems engineeringAsset managementData exchangeReference architectureSoftware architectureDatabaseSoftwareEngineeringOperating system
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the development of a generic framework for asset maintenance management, and an object model for the maintenance management of roofing systems as a case study to demonstrate the applicability of the framework. The model builds upon the Industry Foundation Classes (IFCs) to define object requirements and relationships for the exchange and sharing of maintenance information between applications. The paper explores the implementation of the developed maintenance management models through the development of a distributed, model-based integrated system. It describes the set of inter-connecting components forming a typical or reference system architecture for integrated distributed systems. It describes the Jigsaw Distributed System (JDS) version 0.6, as the implementation environment of the reference architecture, which facilitates a wide range of data exchanges and software interoperability. The paper describes the development of a generic Asset Management Tool (AMT) prototype data client application that can initiate data exchanges with a number of data servers (including MicroROOFER, Microsoft Project, and files that can be used by several other applications), thus demonstrating software interoperability in the Facilities Management (FM) domain. Finally the paper presents an evaluation and testing scenario for the prototype application.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.281

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations20
Published2003
Admission routes2
Has abstractyes

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