Asset Management and Pavement Management: Using Common Elements to Maximize Overall Benefits
Bibliographic record
Abstract
The public and private sectors have been managing their assets in some form for many years. Recently, however, the concept of asset management has been formulated to draw more explicitly on the principles of business, technology, economics, and other disciplines in a systematic and integrated way. This strategy offers cost-effective and responsive advantages in managing the public’s assets. Other management systems, particularly pavement management, have preceded the current interest in asset management by several decades. Accordingly, it is useful to assess whether there are common elements between asset management and pavement management and, if so, whether the experience gained from pavement management implementation and operation can be of benefit. In a generic sense, asset management has extensive commonalities with its component systems such as pavement management. However, asset management has some issues to resolve in progressing from a framework to an operational reality. A number of ways or areas in which asset management system development and implementation can benefit from pavement management operational experience are presented. Finally, some technical, economic/technical, and institution and user opportunities for innovations and advancements in asset management systems are identified.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".