Tangible capital asset ontology in infrastructure 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
Infrastructure organisations own, operate and manage infrastructure systems to provide uninterrupted services to various communities. To manage infrastructure systems (composed of a set of interrelated and interconnected tangible capital assets (TCAs)), infrastructure organisations use a range of computer and paper-based information systems. Municipal infrastructure organisations find it difficult to exchange the TCA data with other agencies as part of the reporting requirements due to some issues: heterogeneity of data format, lack of formal descriptions of various classes of data and lack of component-wise aggregation of data. To address these issues, an ontology of TCAs was developed using an eleven-step approach. The tangible capital asset ontology (TCA_Onto) represents knowledge in the facility and four infrastructure sectors: transportation, water, wastewater and solid waste management, which was used to formalise message templates for the asset inventory and condition assessment reporting/TCA reporting. The formalised message templates were implemented in a prototype asset information integrator system developed as part of this research work. The TCA_Onto was verified and validated as part of the evaluation using a criteria-based approach.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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