Bibliographic record
Abstract
Water charges in the UK, Ireland, Canada and parts of the USA are largely based upon house value rather than consumption. This paper shows how charges based upon consumption could facilitate trading among metered consumers and provide incentives to non-metered customers to switch to a metered tariff. To develop a household domestic water trading scheme, various environmental trading schemes were examined including the EU emissions trading scheme (EU ETS) and tradable energy quotas (TEQs). The derived scheme encourages domestic consumers to trade and reduce their water use. Although a degree of trading would be possible on an occasional basis for unmetered properties (by the substitution of appliances with water-conserving models) and conventionally metered properties (based upon sustained reductions in consumption, but on an annual basis), frequent full trading would only be possible if smart water meters were installed. It is concluded that, while water trading schemes are feasible, they present a number of challenges. However, due to the availability of a number of existing potential elements, implementation—at least on a regional basis—could be achievable relatively quickly. Once an initial scheme is running it could be refined and expanded to other regions, and nationally or internationally, if appropriate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".