Bibliographic record
Abstract
This paper describes the implementation of an Asset Management Program in a roadway agency. It covers some of the practical challenges and successes that the City of Calgary’s Roads Business Unit achieved in their program. The program was simply called RAMP, short for Roads Asset Management Program. The paper describes the need for asset management at the agency, how the program was created and its main objectives. It then describes how information systems were developed and integrated to achieve the agency's asset management objectives. Integration of financial, work order and asset information systems are critical in achieving these objectives. As most of the city’s assets are geographically dispersed, a GIS (Geographical Information System) is also critical for the agency to effectively manage its assets. The first asset management plan was developed in 2007/8 for the roads agency and the paper describes some of the challenges doing this and how it can be improved into the future. Emphases are placed on improving practices like risk assessment, determining levels of service, doing benchmarking and teaching asset management concepts to the operations personnel. Understanding assets life cycle and having supporting data, proved to be challenges in the process. Tools to create these plans are also needed and extend beyond most work management systems capabilities. The paper will be of interest to practitioners who want to create an asset management orientated organization. It will also appeal to practitioners who want to or are creating asset management plans for their organizations and need to establish asset management systems to provide this information.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".