ASSET VALUATION METHODOLOGIES AND PERFORMANCE MEASUREMENT IN LIFE-CYCLE ANALYSIS
Bibliographic record
Abstract
Pavement management systems, and now more broadly asset management systems, have been accepted and implemented by many agencies worldwide. Life cycle analysis is a key component of these systems at both the project and network level. However, the incorporation of asset value in life cycle analysis has received little attention. Rather, current and future costs are the prime elements. The time has come though where owners or operators of the asses, the latter particularly in the case of privatization, are starting to require the explicit incorporation of asset value in the life cycle analysis. In other words, the issue is what was the asset worth when built, today, and what is it estimated to be in future years under various alternative strategies and funding scenarios. This paper is based on a highway asset valuation and performance indicators study carried out for the Transportation Association of Canada. It describes the role of asset valuation in asset management, the available methodologies and their applicability and the direct incorporation and reporting of asset value in the life cycle analysis. As well, the paper describes the associated performance indicators related to the genera, macro level, service quality to users, functional effectiveness and preservation effectiveness. Finally, the paper identifies the major issues and requirements involved in the proper application or use of asset valuation in life cycle analysis.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".