IMPLEMENTATION OF A DISTRIBUTED, MODEL-BASED INTEGRATED ASSET MANAGEMENT SYSTEM
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".