Asset Management of Urban Drainage Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Asset management issues are and will always be key concerns for many stakeholders in the water sector. Despite this, there is still a lack of awareness and clear guidance on the topic. There has been some focus on the management of drainage pipes, but more effort needs to be dedicated to examining the various regulations, practices, and research within this discipline. It's paramount to consider the long-term management of urban drainage assets, given the role they play in ensuring the wellbeing of our communities. Asset Management of Urban Drainage Systems is the first comprehensive handbook that deals with the asset management of infrastructure dedicated to both sewage and stormwater, including blue-green infrastructure. It gives an insight into the theoretical background of asset management itself and showcases regulations and legislation influencing it. The methods used to investigate the condition of assets, and how they can be modelled and represented while accounting for the associated limitations, are also presented. The book describes how the discipline can move from a purely condition-based approach to a service-based one using risk-management strategies, seen in the broader context of decision-making. Data management and techniques for the rehabilitation of urban drainage assets are also explored. From technicians who want to know more about the tools and methods, to researchers and students who want a broad overview, to professionals who are tasked with developing short, medium, and long-term asset management strategies, this book provides important content for a wide audience. ISBN: 9781789063042 (paperback) ISBN: 9781789063059 (eBook) ISBN: 9781789063066 (ePub)
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it