Operation, Maintenance and Performance
Bibliographic record
Abstract
This chapter commences by describing the development of water distribution pipe networks to provide a clean supply of wholesome drinking water to ensure public health through to the more recent operational drivers associated with water quantity, for example, reduced leakage and minimal energy use, and water quality, for example, a reduced number of customer contacts. It is this piece-meal development, rather than idealised design, as presented in the last chapter, that is often the primary cause of many of the operational, maintenance and performance challenges that face the water industry. A focus has also been given to an overview of the regulations and standards, which range from a need to meet stringent regulatory standards to the softer measures of customer expectations and customer orientated care. The main body of the chapter is structured around the operation and maintenance cycle under the ‘MAIDE’ (Monitoring, Analysis, Interventions, Decision and Evaluations) concept of five core elements. Though more commonly in use now is the PALMM approach (Prevention, Awareness, Location, Mitigation Mend). Techniques and approaches are discussed with reference to both the quantity of water, including loss of supply and leakage, and the quality of water. The argument is developed, firstly by reference to historical tried and tested methodologies, through the latest developments and approaches to optimise system performance, operation, and maintenance.
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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".