Multi-Tiered Database Schema for Integrated Municipal Asset Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 it