Rules and regulations
Bibliographic record
Abstract
Abstract This chapter covers the legal framework and technical regulations that must or should be observed for the strategic asset management (AM) of urban drainage systems over the entire life cycle (planning, construction, maintenance, and dismantling). A distinction is made between rules and regulations that deal with network management in general (strategic level) and rules and regulations that address the management of individual network components and/or certain activities (e.g., CCTV-inspection and condition assessment of reaches or stormwater basins). These activities are subsumed under ‘operative level’. It should be noted that both the legal framework and the applicable technical regulations vary widely from region to region. In some cases, different regulations apply even in different provinces or federal states of a country. Against this background, only case studies can (such as regulations that apply to Germany, France, Colombia or Canada) and will be presented in this chapter. It is thus made clear that AM in the sense of ISO 55000 to 55002 (AM) enables a structured approach to a multi-layered field of tasks. In this way, goals and conflicting goals can be identified and prioritized at various levels and, in conjunction with the continuous improvement process in accordance with ISO 9000 and 9001 (Quality Management Systems), efficient ways can be found to achieve these goals.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".