Data modeling standards for developing interoperable municipal asset management systems
Bibliographic record
Abstract
Sustainable management of municipal infrastructure assets depends to a large extent on the ability toefficiently share, exchange, and manage life cycle information concerning the assets. Although software tools are used to support almost every asset management process in municipalities, data exchange is mainly done using paperbased or neutral file formats based on ad-hoc proprietary data models. The inability of municipal asset management systems to interoperate creates inefficiencies and impedes sustainability. Interoperability of various asset management systems is crucial to support better management of infrastructure data, to improve information flow and to streamline municipal workflow processes. This paper surveys a number of available data standards that can potentially be used for implementing interoperable and integrated municipal asset management systems. The paper outlines the main requirements for standard data models and highlights the importance of interoperability from an asset management perspective. The paper also discusses the role that spatial data and GIS can play in enhancing the municipal asset management processes by increasing the efficiency of managing the asset data. Relevant efforts to develop and standardize data models for municipal assets are also presented. The paper argues that using standard data models can significantly improve the availability and consistency of the asset data across different software systems and platforms, can serve to integrate data across various disciplines, and can facilitate the flow and exchange of information between various parties involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".