Developments in water resources planning in the United Kingdom: balancing bottom-up and top-down approaches
Bibliographic record
Abstract
Water companies in the United Kingdom are required to produce long-term plans of water resources for their supply area every five years, outlining how they plan to maintain secure and sustainable supplies, taking account of social and environmental impacts as well as economic costs. As a result, the water environment is highly regulated to ensure competing demands are satisfied. A quarter of the population lives in the south-east of the country, with water supplied by six different water companies. This region faces long term challenges of population growth, which is projected to grow at a rate exceeding the national average, and some areas predicted to face water supply deficits in the near future. \nThe recent WaterUK long term planning framework report concluded that large-scale inter-regional transfers of water could offer the best value to securing water resources on a national scale. However, the planning guidelines suggest that the complexity of the water resources planning method applied is proportional to the challenges faced by the individual water company. With the first regional water resource plans programmed for publication in 2022, regional bodies are facing the challenge of amalgamating results from the wide range of methods applied within their region. The absence of a one size fits all approach poses difficulties in assessing and modelling the viability and timing of such schemes. \n \nWe address how water companies and regional bodies are working together to produce integrated regional water resources and investment models to arrive at optimal solutions. Recent work by HR Wallingford demonstrates the benefits and limitations of such an optimised approach, as well as highlighting the initial challenges of planning a strategic inter-basin water transfer from the perspectives of water companies, regulators, and stakeholders in a region facing some of the most challenging water resources issues in the country.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".