Tangible capital asset ontology in infrastructure management
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".