Operation, maintenance and rehabilitation techniques
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 Urban drainage operation, management and rehabilitation can be divided into two distinct segments: traditional grey infrastructures (i.e. pipes and associated components) and green infrastructures. For piped systems this boils down to maintaining the operational safety, stability and tightness of the sewers and special structures. However, this chapter provides an overview on both realms and highlights that, while there is a lot of standardization for grey infrastructures, the knowledge on green ones is much more fractured. They are often composed of both engineered and natural elements such as pipes, flow control systems, vegetation, micro-organisms in the soil or growing media, and also deliver a broad range of beneficial services to our communities and their inhabitants. Existing terminology for pipe networks is adapted by defining a similar distinction for green infrastructures based on the severity of the necessary actions. There will be no focus on other special structures and machinery. Adopting these distinctions, this chapter consists of three parts: (1) pipe network operation and maintenance (O&M), (2) structural rehabilitation of pipe networks and the connected manholes and (3) green infrastructure rehabilitation including O&M focusing on some examples. Consequently, this chapter can be used as guidance on available technologies, existing guidelines and research gaps.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it