Concept of unification of mutually incompatible information models and data stored in relational databases of road administrations
Bibliographic record
Abstract
Road Administrations (RAs) implement Building Information Modelling (BIM) through pilot projects developed for new or reconstructed structures. Each model is processed with respect to BIM standards and practices valid at the time of its creation. Consequently, models are incompatible and cannot be interconnected to create a combined model of the managed network or even its selected parts. Existing structures are often not included in the BIM effort until some major repair is planned. In addition, RAs usually store data on fixed and variable parameters of structures in relational databases. This results in a situation in which a relatively small number of structures are included in mutually incompatible models and data regarding the majority of structures is contained in relational databases. It creates a heterogeneous data environment for RAs. The goals of the paper are as follows: to analyse the described problem, to propose a method of model unification models, a method of creation of simplified compatible information models using data on existing structures stored in relational databases and a method of storing data at the level of the managed network, to support RA asset management systems which can be treated as a dynamic part of BIM.
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 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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".