Knowledge Enabled Tangible Capital Asset Reporting Using Asset Information Integrator System in Infrastructure Management
Bibliographic record
Abstract
For effective management of the infrastructure systems, municipalities need to efficiently exchange information about these systems (i.e tangible capital asset information) within different departments of a municipality and between different agencies. This information exchange, referred to as transactions, is presently performed on an ad hoc and manual basis in the form of non-standardized reports created in PDF or Word format. Municipal organizations find it very difficult and time-consuming to compile, compare, extract, and analyze the Asset Inventory and Condition Assessment/Tangible Capital Asset information from these reports due to: (i) human interpretational issues; (ii) heterogeneity of the tangible capital asset data format; (iii) inconsistent description of various classes of assets; and (iv) lack of component-based aggregation of assets. There is a need to improve the way Asset Inventory and Condition Assessment Reporting or Tangible Capital Asset Reporting is currently performed by infrastructure agencies. To address these issues, a Tangible Capital Asset Ontology was developed as part of this research work. The knowledge represented in the ontology was used to define message templates that permit standardized reporting of the asset inventory and condition assessment/tangible capital asset information. Using a four step approach, the formalized message templates were implemented in an Asset Information Integrator System developed as part of this research. The focus of this paper is to introduce the Tangible Capital Asset Kernel Ontology and explain the development and application of the Asset Information Integrator System to demonstrate improved tangible capital asset reporting. The technique employs a case study 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".