Asset Management Mixing Bowl: Idea Sharing Amongst Owners
Bibliographic record
Abstract
Many municipalities have the same concerns when it comes to infrastructure planning. Whether it is condition assessment, budgeting or general planning concerns, most similar sized agencies face the same dilemmas throughout the U.S. The knowledge gained through active communication between those sharing a similar interest can be vast. It is likely that challenges being faced by one owner are also being experienced by many more similarly sized agencies. Howard County Department of Public Works (DWP) recently completed a condition assessment for over 44,000 LF of distribution main in one of its oldest planned communities. The Wilde Lake area, which was established in the mid-1960’s, has experienced numerous water main breaks in recent years. In an effort to remain pro-active, Howard County DPW launched a comprehensive study into the cause of the water main breaks, with the intent of developing an overall replacement strategy for the community. As the project developed, it became apparent that this community’s distribution system was a good representation of the County’s system as a whole and the studies completed as part of this project could be applied comprehensively to the entire system. As such, the Wilde Lake condition assessment became a pilot program which could be used to develop a larger asset management program for their distribution system. As the project continued, it became apparent that other local agencies were facing a similar dilemma of how to evaluate and manage their distribution systems. In an effort to gain an industry-wide perspective, the County developed a “Pipeline Management Working Group” that included representatives from Baltimore County, Baltimore City, DC Water and WSSC. These agencies met both in person and via webinar to discuss topics such as: (1) Various inspection techniques (2) Desktop pipeline risk analysis (3) Data Management (4) Replacement strategies (5) Operational strategies. Following the successful outcome of the local information sharing session, the program was expanded to include other North American utility owners, when Howard County hosted a Pipeline Management Working Group at the 2014 ASCE Pipelines Conference in Portland, OR. This session was attended by owners from the US and Canada, all of whom shared a common interest in learning how each other handled their distribution systems. The idea of information sharing, although not a new concept, it is typically done only on a local level. However, by expanding the circle of participants to those outside a local region, additional perspectives can be gained. As the mixing bowl continues to grow to include additional participants, the level of quality knowledge being exchanged is sure to reach new heights!
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".