NRCC/CPWA/IPWEA Seminar Series "Innovations in Urban Infrastructure"
Bibliographic record
Abstract
Today, there exists an estimated $33 trillion worth of constructed assets in North America, with 20 percent of that asset base invested in municipal infrastructure. In North America and Australia, there are hundreds of billions of dollars in backlogged maintenance, and a large portion of the asset base must be considered for capital renewal in the near future. This paper and the remainder of the papers in this Asset Management session take an in-depth look, on a discipline by-discipline basis, at how to reach strategic solutions to managing, maintaining and renewing this vital infrastructure. In the associated six papers, the domain experts will concentrate on the overall goals of strategic asset management and its application to water and waste water systems, bridge projects and networks, building maintenance, and roadway infrastructure. This panel of international speakers address the six essential areas of asset management in their domain of interest: 1) asset inventory techniques, 2) asset valuation methods, 3) deferred maintenance classification, 4) condition assessment surveys, 5) service life prediction models, and 6) maintenance planning strategies.The goal of my paper is to introduce the reader to the topic of strategic asset management, to provide a vocabulary of terms used in the domain, to provide a background for the need for asset management, to describe a sequential process for the proper implementation of an asset management system, and to describe an innovative project related to municipal infrastructure investment planning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.135 | 0.027 |
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".