Making More Sustainable Decisions for Asset Investment in the Water Industry - Sustainable Water Industry Asset Resource Decisions - The SWARD Project
Bibliographic record
Abstract
Effective Integrated Water Management (IWM) is an aspiration for all those engaged in water service provision, and is a key component of the World Water Vision. Part of this includes the sustainability of water systems and their interaction with other urban systems. In the urban drainage field, there are many examples of attempts to establish effective integrated systems. A major problem, however, is the elusive nature of the concept of sustainability and how to translate what is known in terms of sustainability principles and objectives into action within the IWM perspective. Case studies are presented that illustrate how urban drainage problems can be approached in a way that takes due account of sustainability considerations. These studies utilise a new Guidebook that presents multi-criteria decision support systems to assist Water Service Providers (WSPs) to assess the relative sustainability of water/wastewater system asset development decisions. The Guidebook was developed as part of a UK government and industry funded multi-partner project over the past 4 years. An essential feature of the Guidebook is its transparency, as it is intended to be accessible to all stakeholders affected by a proposed development.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".