Multi-Tiered Database Schema for Integrated Municipal Asset Management
Bibliographic record
Abstract
Municipality Engineers and decision makers have to consult and utilize large data sets to make informed management decisions concerning individual and/or integrated infrastructure assets. Such data is gathered and processed more than once, by different users; furthermore, it is usually stored in different repositories and in different formats. This reality necessitates the design and utilization of databases that are specially structured to facilitate the management of such massive volumes of interrelated data. This paper presents a multi-tiered database schema for integrated infrastructure management. The schema is multi-tiered to be usable by different size municipalities; adopting different approaches for asset management. The presented schema comprises a basic tier for managing basic data of separate assets through running common asset management processes, a second tier for running advanced asset management models, and a final tier for managing special data required for specific usage. The database schema can be utilized for managing basic data of separate assets, and its full scale implementation allows managing integrated assets using data generated by different up-to-date asset management tools. The database schema is implemented in ArcGIS and applied to a municipal database containing data for water, sewer and road networks to demonstrate its applicability and essential features.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".